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Investigation of mental fatigue through EEG signal processing based on nonlinear analysis: Symbolic dynamics
International Nuclear Information System (INIS)
Azarnoosh, Mahdi; Motie Nasrabadi, Ali; Mohammadi, Mohammad Reza; Firoozabadi, Mohammad
Highlights: Mental fatigue indices' variation discussed during simple long-term attentive task. Symbolic dynamics of reaction time and EEG signal determine mental state variation. Nonlinear quantifiers such as entropy can display chaotic behaviors of the brain. Frontal and central lobes of the brain are effective in attention investigations. Mental fatigue causes a reduction in the complexity of the brain's activity. Abstract: To investigate nonlinear analysis of attention physiological indices this study used a simple repetitive attentive task in four consecutive trials that resulted in mental fatigue. Traditional performance indices, such as reaction time, error responses, and EEG signals, were simultaneously recorded to evaluate differences between the trials. Performance indices analysis demonstrated that a selected task leads to mental fatigue. In addition, the study aimed to find a method to determine mental fatigue based on nonlinear analysis of EEG signals. Symbolic dynamics was selected as a qualitative method used to extract some quantitative qualifiers such as entropy. This method was executed on the reaction time of responses, and EEG signals to distinguish mental states. The results revealed that nonlinear analysis of reaction time, and EEG signals of the frontal and central lobes of the brain could differentiate between attention, and occurrence of mental fatigue in trials. In addition, the trend of entropy variation displayed a reduction in the complexity of mental activity as fatigue occurred.
Study on non-linear bistable dynamics model based EEG signal discrimination analysis method.
Ying, Xiaoguo; Lin, Han; Hui, Guohua
Electroencephalogram (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations generating from ionic current flows within the neurons of the brain. EEG signal is looked as one of the most important factors that will be focused in the next 20 years. In this paper, EEG signal discrimination based on non-linear bistable dynamical model was proposed. EEG signals were processed by non-linear bistable dynamical model, and features of EEG signals were characterized by coherence index. Experimental results showed that the proposed method could properly extract the features of different EEG signals.
Non-linear Analysis of Scalp EEG by Using Bispectra: The Effect of the Reference Choice
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Federico Chella
Full Text Available Bispectral analysis is a signal processing technique that makes it possible to capture the non-linear and non-Gaussian properties of the EEG signals. It has found various applications in EEG research and clinical practice, including the assessment of anesthetic depth, the identification of epileptic seizures, and more recently, the evaluation of non-linear cross-frequency brain functional connectivity. However, the validity and reliability of the indices drawn from bispectral analysis of EEG signals are potentially biased by the use of a non-neutral EEG reference. The present study aims at investigating the effects of the reference choice on the analysis of the non-linear features of EEG signals through bicoherence, as well as on the estimation of cross-frequency EEG connectivity through two different non-linear measures, i.e., the cross-bicoherence and the antisymmetric cross-bicoherence. To this end, four commonly used reference schemes were considered: the vertex electrode (Cz, the digitally linked mastoids, the average reference, and the Reference Electrode Standardization Technique (REST. The reference effects were assessed both in simulations and in a real EEG experiment. The simulations allowed to investigated: (i the effects of the electrode density on the performance of the above references in the estimation of bispectral measures; and (ii the effects of the head model accuracy in the performance of the REST. For real data, the EEG signals recorded from 10 subjects during eyes open resting state were examined, and the distortions induced by the reference choice in the patterns of alpha-beta bicoherence, cross-bicoherence, and antisymmetric cross-bicoherence were assessed. The results showed significant differences in the findings depending on the chosen reference, with the REST providing superior performance than all the other references in approximating the ideal neutral reference. In conclusion, this study highlights the importance of
Nonlinear analysis of EEGs of patients with major depression during different emotional states.
Akdemir Akar, Saime; Kara, Sadık; Agambayev, Sümeyra; Bilgiç, Vedat
Although patients with major depressive disorder (MDD) have dysfunctions in cognitive behaviors and the regulation of emotions, the underlying brain dynamics of the pathophysiology are unclear. Therefore, nonlinear techniques can be used to understand the dynamic behavior of the EEG signals of MDD patients. To investigate and clarify the dynamics of MDD patients׳ brains during different emotional states, EEG recordings were analyzed using nonlinear techniques. The purpose of the present study was to assess whether there are different EEG complexities that discriminate between MDD patients and healthy controls during emotional processing. Therefore, nonlinear parameters, such as Katz fractal dimension (KFD), Higuchi fractal dimension (HFD), Shannon entropy (ShEn), Lempel-Ziv complexity (LZC) and Kolmogorov complexity (KC), were computed from the EEG signals of two groups under different experimental states: noise (negative emotional content) and music (positive emotional content) periods. First, higher complexity values were generated by MDD patients relative to controls. Significant differences were obtained in the frontal and parietal scalp locations using KFD (pemotional bias was demonstrated by their higher brain complexities during the noise period than the music stimulus. Additionally, we found that the KFD, HFD and LZC values were more sensitive in discriminating between patients and controls than the ShEn and KC measures, according to the results of ANOVA and ROC calculations. It can be concluded that the nonlinear analysis may be a useful and discriminative tool in investigating the neuro-dynamic properties of the brain in patients with MDD during emotional stimulation. Copyright © 2015 Elsevier Ltd. All rights reserved.
The cortical focus in childhood absence epilepsy; evidence from nonlinear analysis of scalp EEG recordings.
Sarrigiannis, Ptolemaios G; Zhao, Yifan; He, Fei; Billings, Stephen A; Baster, Kathleen; Rittey, Chris; Yianni, John; Zis, Panagiotis; Wei, Hualiang; Hadjivassiliou, Marios; Grünewald, Richard
To determine the origin and dynamic characteristics of the generalised hyper-synchronous spike and wave (SW) discharges in childhood absence epilepsy (CAE). We applied nonlinear methods, the error reduction ratio (ERR) causality test and cross-frequency analysis, with a nonlinear autoregressive exogenous (NARX) model, to electroencephalograms (EEGs) from CAE, selected with stringent electro-clinical criteria (17 cases, 42 absences). We analysed the pre-ictal and ictal strength of association between homologous and heterologous EEG derivations and estimated the direction of synchronisation and corresponding time lags. A frontal/fronto-central onset of the absences is detected in 13 of the 17 cases with the highest ictal strength of association between homologous frontal followed by centro-temporal and fronto-central areas. Delays consistently in excess of 4 ms occur at the very onset between these regions, swiftly followed by the emergence of "isochronous" (0-2 ms) synchronisation but dynamic time lag changes occur during SW discharges. In absences an initial cortico-cortical spread leads to dynamic lag changes to include periods of isochronous interhemispheric synchronisation, which we hypothesize is mediated by the thalamus. Absences from CAE show ictal epileptic network dynamics remarkably similar to those observed in WAG/Rij rats which guided the formulation of the cortical focus theory. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms
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Cauchy Pradhan
Full Text Available The fundamental nature of the brain's electrical activities recorded as electroencephalogram (EEG remains unknown. Linear stochastic models and spectral estimates are the most common methods for the analysis of EEG because of their robustness, simplicity of interpretation, and apparent association with rhythmic behavioral patterns in nature. In this paper, we extend the use of higher-order spectrum in order to indicate the hidden characteristics of EEG signals that simply do not arise from random processes. The higher-order spectrum is an extension Fourier spectrum that uses higher moments for spectral estimates. This essentially nullifies all Gaussian random effects, therefore, can reveal non-Gaussian and nonlinear characteristics in the complex patterns of EEG time series. The paper demonstrates the distinguishing features of bispectral analysis for chaotic systems, filtered noises, and normal background EEG activity. The bispectrum analysis detects nonlinear interactions; however, it does not quantify the coupling strength. The squared bicoherence in the nonredundant region has been estimated to demonstrate nonlinear coupling. The bicoherence values are minimal for white Gaussian noises (WGNs and filtered noises. Higher bicoherence values in chaotic time series and normal background EEG activities are indicative of nonlinear coupling in these systems. The paper shows utility of bispectral methods as an analytical tool in understanding neural process underlying human EEG patterns.
Nonlinear Analysis of the Sleep EEG in Children with Pervasive Developmental Disorder
Czech Academy of Sciences Publication Activity Database
Kulíšek, R.; Hrnčíř, Z.; Hrdlička, M.; Faladová, L.; Štěrbová, K.; Kršek, P.; Vymlátilová, E.; Paluš, Milan; Zumrová, A.; Komárek, V.
Roč. 29, č. 4 (2008), s. 512-517 ISSN 0172-780X Institutional research plan: CEZ:AV0Z10300504 Keywords : EEG * synchronization * autism * underconnectivity model Subject RIV: FH - Neurology Impact factor: 1.359, year: 2008
Nonlinear Recurrent Dynamics and Long-Term Nonstationarities in EEG Alpha Cortical Activity: Implications for Choosing Adequate Segment Length in Nonlinear EEG Analyses.
Cerquera, Alexander; Vollebregt, Madelon A; Arns, Martijn
Nonlinear analysis of EEG recordings allows detection of characteristics that would probably be neglected by linear methods. This study aimed to determine a suitable epoch length for nonlinear analysis of EEG data based on its recurrence rate in EEG alpha activity (electrodes Fz, Oz, and Pz) from 28 healthy and 64 major depressive disorder subjects. Two nonlinear metrics, Lempel-Ziv complexity and scaling index, were applied in sliding windows of 20 seconds shifted every 1 second and in nonoverlapping windows of 1 minute. In addition, linear spectral analysis was carried out for comparison with the nonlinear results. The analysis with sliding windows showed that the cortical dynamics underlying alpha activity had a recurrence period of around 40 seconds in both groups. In the analysis with nonoverlapping windows, long-term nonstationarities entailed changes over time in the nonlinear dynamics that became significantly different between epochs across time, which was not detected with the linear spectral analysis. Findings suggest that epoch lengths shorter than 40 seconds neglect information in EEG nonlinear studies. In turn, linear analysis did not detect characteristics from long-term nonstationarities in EEG alpha waves of control subjects and patients with major depressive disorder patients. We recommend that application of nonlinear metrics in EEG time series, particularly of alpha activity, should be carried out with epochs around 60 seconds. In addition, this study aimed to demonstrate that long-term nonlinearities are inherent to the cortical brain dynamics regardless of the presence or absence of a mental disorder.
Characterizing nonlinearity in invasive EEG recordings from temporal lobe epilepsy
Casdagli, M. C.; Iasemidis, L. D.; Sackellares, J. C.; Roper, S. N.; Gilmore, R. L.; Savit, R. S.
Invasive electroencephalographic (EEG) recordings from depth and subdural electrodes, performed in eight patients with temporal lobe epilepsy, are analyzed using a variety of nonlinear techniques. A surrogate data technique is used to find strong evidence for nonlinearities in epileptogenic regions of the brain. Most of these nonlinearities are characterized as "spiking" by a wavelet analysis. A small fraction of the nonlinearities are characterized as "recurrent" by a nonlinear prediction algorithm. Recurrent activity is found to occur in spatio-temporal patterns related to the location of the epileptogenic focus. Residual delay maps, used to characterize "lag-one nonlinearity", are remarkably stationary for a given electrode, and exhibit striking variations among electrodes. The clinical and theoretical implications of these results are discussed.
Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.
Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza
Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
Correlation of BOLD Signal with Linear and Nonlinear Patterns of EEG in Resting State EEG-Informed fMRI
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Galina V. Portnova
Full Text Available Concurrent EEG and fMRI acquisitions in resting state showed a correlation between EEG power in various bands and spontaneous BOLD fluctuations. However, there is a lack of data on how changes in the complexity of brain dynamics derived from EEG reflect variations in the BOLD signal. The purpose of our study was to correlate both spectral patterns, as linear features of EEG rhythms, and nonlinear EEG dynamic complexity with neuronal activity obtained by fMRI. We examined the relationships between EEG patterns and brain activation obtained by simultaneous EEG-fMRI during the resting state condition in 25 healthy right-handed adult volunteers. Using EEG-derived regressors, we demonstrated a substantial correlation of BOLD signal changes with linear and nonlinear features of EEG. We found the most significant positive correlation of fMRI signal with delta spectral power. Beta and alpha spectral features had no reliable effect on BOLD fluctuation. However, dynamic changes of alpha peak frequency exhibited a significant association with BOLD signal increase in right-hemisphere areas. Additionally, EEG dynamic complexity as measured by the HFD of the 2–20 Hz EEG frequency range significantly correlated with the activation of cortical and subcortical limbic system areas. Our results indicate that both spectral features of EEG frequency bands and nonlinear dynamic properties of spontaneous EEG are strongly associated with fluctuations of the BOLD signal during the resting state condition.
Gasinski, Leszek
Hausdorff Measures and Capacity. Lebesgue-Bochner and Sobolev Spaces. Nonlinear Operators and Young Measures. Smooth and Nonsmooth Analysis and Variational Principles. Critical Point Theory. Eigenvalue Problems and Maximum Principles. Fixed Point Theory.
The Implicit Function as Squashing Time Model: A Novel Parallel Nonlinear EEG Analysis Technique Distinguishing Mild Cognitive Impairment and Alzheimer's Disease Subjects with High Degree of Accuracy
Directory of Open Access Journals (Sweden)
Massimo Buscema
(2007, this protocol includes a new type of artificial organism, named TWIST. The working hypothesis was that compared to the results presented by the workgroup (2007; the new artificial organism TWIST could produce a better classification between AD and MCI. Material and methods. Resting eyes-closed EEG data were recorded in 180 AD patients and in 115 MCI subjects. The data inputs for the classification, instead of being the EEG data, were the weights of the connections within a nonlinear autoassociative ANN trained to generate the recorded data. The most relevant features were selected and coincidently the datasets were split in the two halves for the final binary classification (training and testing performed by a supervised ANN. Results. The best results distinguishing between AD and MCI were equal to 94.10% and they are considerable better than the ones reported in our previous study (∼92% (2007. Conclusion. The results confirm the working hypothesis that a correct automatic classification of MCI and AD subjects can be obtained by extracting spatial information content of the resting EEG voltage by ANNs and represent the basis for research aimed at integrating spatial and temporal information content of the EEG.
Directory of Open Access Journals (Sweden)
M. Murugesan
Full Text Available Brain is the wonderful organ of human body. It is the agent of information collection and transformation. The neural activity of the human brain starts between the 17th and 23rd week of prenatal development. It is believed that from this early stage and throughout life electrical signals are generated by the brain function but also the status of the whole body. Understanding of neuronal functions and neurophysiologic properties of the brain function together with the mechanisms underlying the generation of signals and their recording is, however, vital for those who deal with these signals for detection, diagnosis, and treatment of brain disorders and the related diseases. This research paper concentrated only on brain tumor detection. Using minimum electrode location the brain tumor possibility is detected. This paper is separated into two parts: the First part deals with electrode location on the scalp and the second part deals with how the fuzzy logic rule based algorithm is applied for estimation of brain tumor from EEG. Basically 8 locations are identified. After acquiring the pure EEG signal Fuzzy Logic Rule is applied to predict the possibility of brain tumor.
Quantitative EEG analysis using error reduction ratio-causality test; validation on simulated and real EEG data.
Sarrigiannis, Ptolemaios G; Zhao, Yifan; Wei, Hua-Liang; Billings, Stephen A; Fotheringham, Jayne; Hadjivassiliou, Marios
To introduce a new method of quantitative EEG analysis in the time domain, the error reduction ratio (ERR)-causality test. To compare performance against cross-correlation and coherence with phase measures. A simulation example was used as a gold standard to assess the performance of ERR-causality, against cross-correlation and coherence. The methods were then applied to real EEG data. Analysis of both simulated and real EEG data demonstrates that ERR-causality successfully detects dynamically evolving changes between two signals, with very high time resolution, dependent on the sampling rate of the data. Our method can properly detect both linear and non-linear effects, encountered during analysis of focal and generalised seizures. We introduce a new quantitative EEG method of analysis. It detects real time levels of synchronisation in the linear and non-linear domains. It computes directionality of information flow with corresponding time lags. This novel dynamic real time EEG signal analysis unveils hidden neural network interactions with a very high time resolution. These interactions cannot be adequately resolved by the traditional methods of coherence and cross-correlation, which provide limited results in the presence of non-linear effects and lack fidelity for changes appearing over small periods of time. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
EEG analysis in a telemedical virtual world
Jovanov, E.; Starcevic, D.; Samardzic, A.; Marsh, A.; Obrenovic, Z.
Telemedicine creates virtual medical collaborative environments. We propose here a novel concept of virtual medical devices (VMD) for telemedical applications. VMDs provide different views on biomedical recordings and efficient signal analysis. In this paper we present a telemedical EEG analysis
Linear and Nonlinear Analysis of Brain Dynamics in Children with Cerebral Palsy
Sajedi, Firoozeh; Ahmadlou, Mehran; Vameghi, Roshanak; Gharib, Masoud; Hemmati, Sahel
This study was carried out to determine linear and nonlinear changes of brain dynamics and their relationships with the motor dysfunctions in CP children. For this purpose power of EEG frequency bands (as a linear analysis) and EEG fractality (as a nonlinear analysis) were computed in eyes-closed resting state and statistically compared between 26…
Nonlinear CER Anticipation of Epileptic Seizures is More Effective in Intracranial EEG than in Scalp/Sphenoidal EEG
Czech Academy of Sciences Publication Activity Database
Komárek, V.; Hrnčíř, Z.; Paluš, Milan; Procházka, T.; Jiruška, P.; Marusic, P.
Roč. 44, Suppl. 8 (2003), s. 178 ISSN 0013-9580. [International Epilepsy Congress /25./. 12.10.2003-16.10.2003, Lisbon] Institutional research plan: CEZ:AV0Z1030915; MSM 111300003; MSM 111300004 Keywords : brain dynamic * long term EEG analysis Subject RIV: ED - Physiology
... brain dead. EEG cannot be used to measure intelligence. Normal Results Brain electrical activity has a certain ... 2018, A.D.A.M., Inc. Duplication for commercial use must be authorized in writing by ADAM ...
African Journals Online (AJOL)
Sep 3, 2017 ... However, very few studies have examined the use of EEG in developing countries, including Ni- ... of evoked potentials from brain neurons, referred to as .... Percentage. Gender. Male. 89. 62.7. Female. 53. 37.3. Age. 0-10. 59.
Multidimensional nonlinear descriptive analysis
Nishisato, Shizuhiko
Quantification of categorical, or non-numerical, data is a problem that scientists face across a wide range of disciplines. Exploring data analysis in various areas of research, such as the social sciences and biology, Multidimensional Nonlinear Descriptive Analysis presents methods for analyzing categorical data that are not necessarily sampled randomly from a normal population and often involve nonlinear relations. This reference not only provides an overview of multidimensional nonlinear descriptive analysis (MUNDA) of discrete data, it also offers new results in a variety of fields. The first part of the book covers conceptual and technical preliminaries needed to understand the data analysis in subsequent chapters. The next two parts contain applications of MUNDA to diverse data types, with each chapter devoted to one type of categorical data, a brief historical comment, and basic skills peculiar to the data types. The final part examines several problems and then concludes with suggestions for futu...
Combined process automation for large-scale EEG analysis.
Sfondouris, John L; Quebedeaux, Tabitha M; Holdgraf, Chris; Musto, Alberto E
Epileptogenesis is a dynamic process producing increased seizure susceptibility. Electroencephalography (EEG) data provides information critical in understanding the evolution of epileptiform changes throughout epileptic foci. We designed an algorithm to facilitate efficient large-scale EEG analysis via linked automation of multiple data processing steps. Using EEG recordings obtained from electrical stimulation studies, the following steps of EEG analysis were automated: (1) alignment and isolation of pre- and post-stimulation intervals, (2) generation of user-defined band frequency waveforms, (3) spike-sorting, (4) quantification of spike and burst data and (5) power spectral density analysis. This algorithm allows for quicker, more efficient EEG analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.
Analysis of EEG Related Saccadic Eye Movement
Funase, Arao; Kuno, Yoshiaki; Okuma, Shigeru; Yagi, Tohru
Our final goal is to establish the model for saccadic eye movement that connects the saccade and the electroencephalogram(EEG). As the first step toward this goal, we recorded and analyzed the saccade-related EEG. In the study recorded in this paper, we tried detecting a certain EEG that is peculiar to the eye movement. In these experiments, each subject was instructed to point their eyes toward visual targets (LEDs) or the direction of the sound sources (buzzers). In the control cases, the EEG was recorded in the case of no eye movemens. As results, in the visual experiments, we found that the potential of EEG changed sharply on the occipital lobe just before eye movement. Furthermore, in the case of the auditory experiments, similar results were observed. In the case of the visual experiments and auditory experiments without eye movement, we could not observed the EEG changed sharply. Moreover, when the subject moved his/her eyes toward a right-side target, a change in EEG potential was found on the right occipital lobe. On the contrary, when the subject moved his/her eyes toward a left-side target, a sharp change in EEG potential was found on the left occipital lobe.
Linear and non-linear interdependence of EEG and HRV frequency bands in human sleep.
Chaparro-Vargas, Ramiro; Dissanayaka, P Chamila; Patti, Chanakya Reddy; Schilling, Claudia; Schredl, Michael; Cvetkovic, Dean
The characterisation of functional interdependencies of the autonomic nervous system (ANS) stands an evergrowing interest to unveil electroencephalographic (EEG) and Heart Rate Variability (HRV) interactions. This paper presents a biosignal processing approach as a supportive computational resource in the estimation of sleep dynamics. The application of linear, non-linear methods and statistical tests upon 10 overnight polysomnographic (PSG) recordings, allowed the computation of wavelet coherence and phase locking values, in order to identify discerning features amongst the clinical healthy subjects. Our findings showed that neuronal oscillations θ, α and σ interact with cardiac power bands at mid-to-high rank of coherence and phase locking, particularly during NREM sleep stages.
FFT transformed quantitative EEG analysis of short term memory load.
Singh, Yogesh; Singh, Jayvardhan; Sharma, Ratna; Talwar, Anjana
The EEG is considered as building block of functional signaling in the brain. The role of EEG oscillations in human information processing has been intensively investigated. To study the quantitative EEG correlates of short term memory load as assessed through Sternberg memory test. The study was conducted on 34 healthy male student volunteers. The intervention consisted of Sternberg memory test, which runs on a version of the Sternberg memory scanning paradigm software on a computer. Electroencephalography (EEG) was recorded from 19 scalp locations according to 10-20 international system of electrode placement. EEG signals were analyzed offline. To overcome the problems of fixed band system, individual alpha frequency (IAF) based frequency band selection method was adopted. The outcome measures were FFT transformed absolute powers in the six bands at 19 electrode positions. Sternberg memory test served as model of short term memory load. Correlation analysis of EEG during memory task was reflected as decreased absolute power in Upper alpha band in nearly all the electrode positions; increased power in Theta band at Fronto-Temporal region and Lower 1 alpha band at Fronto-Central region. Lower 2 alpha, Beta and Gamma band power remained unchanged. Short term memory load has distinct electroencephalographic correlates resembling the mentally stressed state. This is evident from decreased power in Upper alpha band (corresponding to Alpha band of traditional EEG system) which is representative band of relaxed mental state. Fronto-temporal Theta power changes may reflect the encoding and execution of memory task.
Detrended Fluctuation Analysis of the Human EEG during Listening to Emotional Music
Institute of Scientific and Technical Information of China (English)
A nonlinear method named detrended fluctuation analysis (DFA) was utilized to investigate the scaling behavior of the human electroencephalogram (EEG) in three emotional music conditions (fear, happiness, sadness) and a rest condition (eyes-closed). The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides β band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the 'approach-withdrawal' model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.
Multifractal analysis of real and imaginary movements: EEG study
Pavlov, Alexey N.; Maksimenko, Vladimir A.; Runnova, Anastasiya E.; Khramova, Marina V.; Pisarchik, Alexander N.
We study abilities of the wavelet-based multifractal analysis in recognition specific dynamics of electrical brain activity associated with real and imaginary movements. Based on the singularity spectra we analyze electroencephalograms (EEGs) acquired in untrained humans (operators) during imagination of hands movements, and show a possibility to distinguish between the related EEG patterns and the recordings performed during real movements or the background electrical brain activity. We discuss how such recognition depends on the selected brain region.
Random matrix analysis of human EEG data
Czech Academy of Sciences Publication Activity Database
Šeba, Petr
Roč. 91, - (2003), s. 198104-1 - 198104-4 ISSN 0031-9007 R&D Projects: GA ČR GA202/02/0088 Institutional research plan: CEZ:AV0Z1010914 Keywords : random matrix theory * EEG signal Subject RIV: BE - Theoretical Physics Impact factor: 7.035, year: 2003
Discriminant Multitaper Component Analysis of EEG
DEFF Research Database (Denmark)
Dyrholm, Mads; Sajda, Paul
the method for predicting the handedness of a subject's button press given multivariate EEG data. We show that our method learns multitapers sensitive to oscillatory activity in the 8–12 Hz range with spatial filters selective for lateralized motor cortex. This finding is consistent with the well-known mu...
Multifractal Detrended Fluctuation Analysis of alpha and theta EEG rhythms with musical stimuli
International Nuclear Information System (INIS)
Maity, Akash Kumar; Pratihar, Ruchira; Mitra, Anubrato; Dey, Subham; Agrawal, Vishal; Sanyal, Shankha; Banerjee, Archi; Sengupta, Ranjan; Ghosh, Dipak
Highlights: • EEG was done to record the brain electrical activity of 10 subjects in response to simple acoustical tanpura stimuli. • Empirical Mode Decomposition (EMD) technique used to make the EEG signal free from blink and other muscular artifacts. • Multifractal Detrended Fluctuation Analysis (MFDFA) performed to assess the complexity of extracted alpha and theta brain rhythms. • The findings show spectral width i.e. complexity of alpha and theta rhythms increase in all the seven frontal locations studied, under the effect of musical stimuli. - Abstract: Electroencephalography (EEG) was performed on 10 participants using a simple acoustical stimuli i.e. a tanpura drone. The tanpura drone is free from any semantic content and is used with a hypothesis that it provides a specific resting environment for the listeners. The EEG data was extracted for all the frontal electrodes viz. F3, F4, F7, F8, Fp1, Fp2 and Fz. Empirical Mode Decomposition (EMD) was applied on the acquired raw EEG signal to make it free from blink as well as other muscular artifacts. Wavelet Transform (WT) technique was used to segregate alpha and theta waves from the denoised EEG signal. Non-linear analysis in the form of Multifractal Detrended Fluctuation Analysis (MFDFA) was carried out on the extracted alpha and theta time series data to study the variation of their complexity. It was found that in all the frontal electrodes alpha as well as theta complexity increases as is evident from the increase of multifractal spectral width. This study is entirely new and gives interesting data regarding neural activation of the alpha and theta brain rhythms while listening to simple acoustical stimuli. The importance of this study lies in the context of emotion quantification using multifractal spectral width as a parameter as well as in the field of cognitive music therapy. The results are discussed in detail.
Global Analysis of Nonlinear Dynamics
Luo, Albert
Global Analysis of Nonlinear Dynamics collects chapters on recent developments in global analysis of non-linear dynamical systems with a particular emphasis on cell mapping methods developed by Professor C.S. Hsu of the University of California, Berkeley. This collection of contributions prepared by a diverse group of internationally recognized researchers is intended to stimulate interests in global analysis of complex and high-dimensional nonlinear dynamical systems, whose global properties are largely unexplored at this time. This book also: Presents recent developments in global analysis of non-linear dynamical systems Provides in-depth considerations and extensions of cell mapping methods Adopts an inclusive style accessible to non-specialists and graduate students Global Analysis of Nonlinear Dynamics is an ideal reference for the community of nonlinear dynamics in different disciplines including engineering, applied mathematics, meteorology, life science, computational science, and medicine.
Multivariate spectral-analysis of movement-related EEG data
International Nuclear Information System (INIS)
Andrew, C. M.
The univariate method of event-related desynchronization (ERD) analysis, which quantifies the temporal evolution of power within specific frequency bands from electroencephalographic (EEG) data recorded during a task or event, is extended to an event related multivariate spectral analysis method. With this method, time courses of cross-spectra, phase spectra, coherence spectra, band-averaged coherence values (event-related coherence, ERCoh), partial power spectra and partial coherence spectra are estimated from an ensemble of multivariate event-related EEG trials. This provides a means of investigating relationships between EEG signals recorded over different scalp areas during the performance of a task or the occurrence of an event. The multivariate spectral analysis method is applied to EEG data recorded during three different movement-related studies involving discrete right index finger movements. The first study investigates the impact of the EEG derivation type on the temporal evolution of interhemispheric coherence between activity recorded at electrodes overlying the left and right sensorimotor hand areas during cued finger movement. The question results whether changes in coherence necessarily reflect changes in functional coupling of the cortical structures underlying the recording electrodes. The method is applied to data recorded during voluntary finger movement and a hypothesis, based on an existing global/local model of neocortical dynamics, is formulated to explain the coherence results. The third study applies partial spectral analysis too, and investigates phase relationships of, movement-related data recorded from a full head montage, thereby providing further results strengthening the global/local hypothesis. (author)
Deimling, Klaus
topics. However, only a modest preliminary knowledge is needed. In the first chapter, where we introduce an important topological concept, the so-called topological degree for continuous maps from subsets ofRn into Rn, you need not know anything about functional analysis. Starting with Chapter 2, where infinite dimensions first appear, one should be familiar with the essential step of consider ing a sequence or a function of some sort as a point in the corresponding vector space of all such sequences or functions, whenever this abstraction is worthwhile. One should also work out the things which are proved in § 7 and accept certain basic principles of linear functional analysis quoted there for easier references, until they are applied in later chapters. In other words, even the 'completely linear' sections which we have included for your convenience serve only as a vehicle for progress in nonlinearity. Another point that makes the text introductory is the use of an essentially uniform mathematical languag...
Singular spectrum analysis of sleep EEG in insomnia.
Aydın, Serap; Saraoǧlu, Hamdi Melih; Kara, Sadık
In the present study, the Singular Spectrum Analysis (SSA) is applied to sleep EEG segments collected from healthy volunteers and patients diagnosed by either psycho physiological insomnia or paradoxical insomnia. Then, the resulting singular spectra computed for both C3 and C4 recordings are assigned as the features to the Artificial Neural Network (ANN) architectures for EEG classification in diagnose. In tests, singular spectrum of particular sleep stages such as awake, REM, stage1 and stage2, are considered. Three clinical groups are successfully classified by using one hidden layer ANN architecture with respect to their singular spectra. The results show that the SSA can be applied to sleep EEG series to support the clinical findings in insomnia if ten trials are available for the specific sleep stages. In conclusion, the SSA can detect the oscillatory variations on sleep EEG. Therefore, different sleep stages meet different singular spectra. In addition, different healthy conditions generate different singular spectra for each sleep stage. In summary, the SSA can be proposed for EEG discrimination to support the clinical findings for psycho-psychological disorders.
Unsupervised EEG analysis for automated epileptic seizure detection
Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad
Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.
Indian Academy of Sciences (India)
The Structures Panel of the Aeronautics Research and Development Board of India ... A great variety of topics was covered, including themes such as nonlinear finite ... or shell structures, and three are on the composite form of construction, ...
A Novel Method for Detection of Epilepsy in Short and Noisy EEG Signals Using Ordinal Pattern Analysis
Directory of Open Access Journals (Sweden)
Iman Veisi
Full Text Available Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. Epilepsy is considered as a dynamical change in nonlinear and complex brain system. The ability of the proposed measure for characterizing the normal and epileptic EEG signals when the signal is short or is contaminated with noise is investigated and compared with some traditional chaos-based measures. Materials and Methods: In the proposed method, the phase space of the time series is reconstructed and then partitioned using ordinal patterns. The partitions can be labeled using a set of symbols. Therefore, the state trajectory is converted to a symbol sequence. A finite state machine is then constructed to model the sequence. A new complexity measure is proposed to detect dynamical changes using the state transition matrix of the state machine. The proposed complexity measure was applied to detect epilepsy in short and noisy EEG signals and the results were compared with some chaotic measures. Results: The results indicate that this complexity measure can distinguish normal and epileptic EEG signals with an accuracy of more than 97% for clean EEG and more than 75% for highly noised EEG signals. Discussion and Conclusion: The complexity measure can be computed in a very fast and easy way and, unlike traditional chaotic measures, is robust with respect to noise corrupting the data. This measure is also capable of dynamical change detection in short time series data.
Nonlinear programming analysis and methods
Avriel, Mordecai
This text provides an excellent bridge between principal theories and concepts and their practical implementation. Topics include convex programming, duality, generalized convexity, analysis of selected nonlinear programs, techniques for numerical solutions, and unconstrained optimization methods.
Multimodal EEG Recordings, Psychometrics and Behavioural Analysis.
Boeijinga, Peter H
High spatial and temporal resolution measurements of neuronal activity are preferably combined. In an overview on how this approach can take shape, multimodal electroencephalography (EEG) is treated in 2 main parts: by experiments without a task and in the experimentally cued working brain. It concentrates first on the alpha rhythm properties and next on data-driven search for patterns such as the default mode network. The high-resolution volumic distributions of neuronal metabolic indices result in distributed cortical regions and possibly relate to numerous nuclei, observable in a non-invasive manner in the central nervous system of humans. The second part deals with paradigms in which nowadays assessment of target-related networks can align level-dependent blood oxygenation, electrical responses and behaviour, taking the temporal resolution advantages of event-related potentials. Evidence-based electrical propagation in serial tasks during performance is now to a large extent attributed to interconnected pathways, particularly chronometry-dependent ones, throughout a chain including a dorsal stream, next ventral cortical areas taking the flow of information towards inferior temporal domains. The influence of aging is documented, and results of the first multimodal studies in neuropharmacology are consistent. Finally a scope on implementation of advanced clinical applications and personalized marker strategies in neuropsychiatry is indicated. © 2016 S. Karger AG, Basel.
Multi-scale symbolic transfer entropy analysis of EEG
Yao, Wenpo; Wang, Jun
From both global and local perspectives, we symbolize two kinds of EEG and analyze their dynamic and asymmetrical information using multi-scale transfer entropy. Multi-scale process with scale factor from 1 to 199 and step size of 2 is applied to EEG of healthy people and epileptic patients, and then the permutation with embedding dimension of 3 and global approach are used to symbolize the sequences. The forward and reverse symbol sequences are taken as the inputs of transfer entropy. Scale factor intervals of permutation and global way are (37, 57) and (65, 85) where the two kinds of EEG have satisfied entropy distinctions. When scale factor is 67, transfer entropy of the healthy and epileptic subjects of permutation, 0.1137 and 0.1028, have biggest difference. And the corresponding values of the global symbolization is 0.0641 and 0.0601 which lies in the scale factor of 165. Research results show that permutation which takes contribution of local information has better distinction and is more effectively applied to our multi-scale transfer entropy analysis of EEG.
EEG and MEG Data Analysis in SPM8
Directory of Open Access Journals (Sweden)
Vladimir Litvak
Full Text Available SPM is a free and open source software written in MATLAB (The MathWorks, Inc.. In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii dynamic causal modelling (DCM, an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra, induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI and batching tools.
EEG and MEG data analysis in SPM8.
Litvak, Vladimir; Mattout, Jérémie; Kiebel, Stefan; Phillips, Christophe; Henson, Richard; Kilner, James; Barnes, Gareth; Oostenveld, Robert; Daunizeau, Jean; Flandin, Guillaume; Penny, Will; Friston, Karl
SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.
Bellman, Richard Ernest
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank mat
Estimating Driving Performance Based on EEG Spectrum Analysis
Directory of Open Access Journals (Sweden)
Jung Tzyy-Ping
Full Text Available The growing number of traffic accidents in recent years has become a serious concern to society. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedbacks to maintain their maximum performance. This paper proposes an EEG-based drowsiness estimation system that combines electroencephalogram (EEG log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver's drowsiness level in a virtual-reality-based driving simulator. Our results demonstrated that it is feasible to accurately estimate quantitatively driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulator.
Joint optimization of algorithmic suites for EEG analysis.
Santana, Eder; Brockmeier, Austin J; Principe, Jose C
Electroencephalogram (EEG) data analysis algorithms consist of multiple processing steps each with a number of free parameters. A joint optimization methodology can be used as a wrapper to fine-tune these parameters for the patient or application. This approach is inspired by deep learning neural network models, but differs because the processing layers for EEG are heterogeneous with different approaches used for processing space and time. Nonetheless, we treat the processing stages as a neural network and apply backpropagation to jointly optimize the parameters. This approach outperforms previous results on the BCI Competition II - dataset IV; additionally, it outperforms the common spatial patterns (CSP) algorithm on the BCI Competition III dataset IV. In addition, the optimized parameters in the architecture are still interpretable.
Erem, B; Hyde, D E; Peters, J M; Duffy, F H; Brooks, D H; Warfield, S K
The dynamical structure of the brain's electrical signals contains valuable information about its physiology. Here we combine techniques for nonlinear dynamical analysis and manifold identification to reveal complex and recurrent dynamics in interictal epileptiform discharges (IEDs). Our results suggest that recurrent IEDs exhibit some consistent dynamics, which may only last briefly, and so individual IED dynamics may need to be considered in order to understand their genesis. This could potentially serve to constrain the dynamics of the inverse source localization problem.
Recognizing emotions from EEG subbands using wavelet analysis.
Candra, Henry; Yuwono, Mitchell; Handojoseno, Ardi; Chai, Rifai; Su, Steven; Nguyen, Hung T
Objectively recognizing emotions is a particularly important task to ensure that patients with emotional symptoms are given the appropriate treatments. The aim of this study was to develop an emotion recognition system using Electroencephalogram (EEG) signals to identify four emotions including happy, sad, angry, and relaxed. We approached this objective by firstly investigating the relevant EEG frequency band followed by deciding the appropriate feature extraction method. Two features were considered namely: 1. Wavelet Energy, and 2. Wavelet Entropy. EEG Channels reduction was then implemented to reduce the complexity of the features. The ground truth emotional states of each subject were inferred using Russel's circumplex model of emotion, that is, by mapping the subjectively reported degrees of valence (pleasure) and arousal to the appropriate emotions - for example, an emotion with high valence and high arousal is equivalent to a `happy' emotional state, while low valence and low arousal is equivalent to a `sad' emotional state. The Support Vector Machine (SVM) classifier was then used for mapping each feature vector into corresponding discrete emotions. The results presented in this study indicated thatWavelet features extracted from alpha, beta and gamma bands seem to provide the necessary information for describing the aforementioned emotions. Using the DEAP (Dataset for Emotion Analysis using electroencephalogram, Physiological and Video Signals), our proposed method achieved an average sensitivity and specificity of 77.4% ± 14.1% and 69.1% ± 12.8%, respectively.
A method for detecting nonlinear determinism in normal and epileptic brain EEG signals.
Meghdadi, Amir H; Fazel-Rezai, Reza; Aghakhani, Yahya
A robust method of detecting determinism for short time series is proposed and applied to both healthy and epileptic EEG signals. The method provides a robust measure of determinism through characterizing the trajectories of the signal components which are obtained through singular value decomposition. Robustness of the method is shown by calculating proposed index of determinism at different levels of white and colored noise added to a simulated chaotic signal. The method is shown to be able to detect determinism at considerably high levels of additive noise. The method is then applied to both intracranial and scalp EEG recordings collected in different data sets for healthy and epileptic brain signals. The results show that for all of the studied EEG data sets there is enough evidence of determinism. The determinism is more significant for intracranial EEG recordings particularly during seizure activity.
Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.
Cichy, Radoslaw Martin; Pantazis, Dimitrios
Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.
Single-trial log transformation is optimal in frequency analysis of resting EEG alpha.
Smulders, Fren T Y; Ten Oever, Sanne; Donkers, Franc C L; Quaedflieg, Conny W E M; van de Ven, Vincent
The appropriate definition and scaling of the magnitude of electroencephalogram (EEG) oscillations is an underdeveloped area. The aim of this study was to optimize the analysis of resting EEG alpha magnitude, focusing on alpha peak frequency and nonlinear transformation of alpha power. A family of nonlinear transforms, Box-Cox transforms, were applied to find the transform that (a) maximized a non-disputed effect: the increase in alpha magnitude when the eyes are closed (Berger effect), and (b) made the distribution of alpha magnitude closest to normal across epochs within each participant, or across participants. The transformations were performed either at the single epoch level or at the epoch-average level. Alpha peak frequency showed large individual differences, yet good correspondence between various ways to estimate it in 2 min of eyes-closed and 2 min of eyes-open resting EEG data. Both alpha magnitude and the Berger effect were larger for individual alpha than for a generic (8-12 Hz) alpha band. The log-transform on single epochs (a) maximized the t-value of the contrast between the eyes-open and eyes-closed conditions when tested within each participant, and (b) rendered near-normally distributed alpha power across epochs and participants, thereby making further transformation of epoch averages superfluous. The results suggest that the log-normal distribution is a fundamental property of variations in alpha power across time in the order of seconds. Moreover, effects on alpha power appear to be multiplicative rather than additive. These findings support the use of the log-transform on single epochs to achieve appropriate scaling of alpha magnitude. © 2018 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
Nonlinear analysis of pupillary dynamics.
Onorati, Francesco; Mainardi, Luca Tommaso; Sirca, Fabiola; Russo, Vincenzo; Barbieri, Riccardo
Pupil size reflects autonomic response to different environmental and behavioral stimuli, and its dynamics have been linked to other autonomic correlates such as cardiac and respiratory rhythms. The aim of this study is to assess the nonlinear characteristics of pupil size of 25 normal subjects who participated in a psychophysiological experimental protocol with four experimental conditions, namely "baseline", "anger", "joy", and "sadness". Nonlinear measures, such as sample entropy, correlation dimension, and largest Lyapunov exponent, were computed on reconstructed signals of spontaneous fluctuations of pupil dilation. Nonparametric statistical tests were performed on surrogate data to verify that the nonlinear measures are an intrinsic characteristic of the signals. We then developed and applied a piecewise linear regression model to detrended fluctuation analysis (DFA). Two joinpoints and three scaling intervals were identified: slope α0, at slow time scales, represents a persistent nonstationary long-range correlation, whereas α1 and α2, at middle and fast time scales, respectively, represent long-range power-law correlations, similarly to DFA applied to heart rate variability signals. Of the computed complexity measures, α0 showed statistically significant differences among experimental conditions (pnonlinear dynamics, (b) three well-defined and distinct long-memory processes exist at different time scales, and (c) autonomic stimulation is partially reflected in nonlinear dynamics. (c) autonomic stimulation is partially reflected in nonlinear dynamics.
Analysis of routine EEG usage in a general adult ICU.
LENUS (Irish Health Repository)
McHugh, J C
Non-convulsive seizures and status epilepticus are common in brain-injured patients in intensive care units. Continuous electroencephalography (cEEG) monitoring is the most sensitive means of their detection. In centres where cEEG is unavailable, routine EEG is often utilized for diagnosis although its sensitivity is lower.
EEG-Informed fMRI: A Review of Data Analysis Methods
Abreu, Rodolfo; Leal, Alberto; Figueiredo, Patrícia
The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest. PMID:29467634
EEG-Informed fMRI: A Review of Data Analysis Methods
Directory of Open Access Journals (Sweden)
Rodolfo Abreu
Full Text Available The simultaneous acquisition of electroencephalography (EEG with functional magnetic resonance imaging (fMRI is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.
Optimizing detection and analysis of slow waves in sleep EEG.
Mensen, Armand; Riedner, Brady; Tononi, Giulio
Analysis of individual slow waves in EEG recording during sleep provides both greater sensitivity and specificity compared to spectral power measures. However, parameters for detection and analysis have not been widely explored and validated. We present a new, open-source, Matlab based, toolbox for the automatic detection and analysis of slow waves; with adjustable parameter settings, as well as manual correction and exploration of the results using a multi-faceted visualization tool. We explore a large search space of parameter settings for slow wave detection and measure their effects on a selection of outcome parameters. Every choice of parameter setting had some effect on at least one outcome parameter. In general, the largest effect sizes were found when choosing the EEG reference, type of canonical waveform, and amplitude thresholding. Previously published methods accurately detect large, global waves but are conservative and miss the detection of smaller amplitude, local slow waves. The toolbox has additional benefits in terms of speed, user-interface, and visualization options to compare and contrast slow waves. The exploration of parameter settings in the toolbox highlights the importance of careful selection of detection METHODS: The sensitivity and specificity of the automated detection can be improved by manually adding or deleting entire waves and or specific channels using the toolbox visualization functions. The toolbox standardizes the detection procedure, sets the stage for reliable results and comparisons and is easy to use without previous programming experience. Copyright © 2016 Elsevier B.V. All rights reserved.
Time frequency analysis of olfactory induced EEG-power change.
Directory of Open Access Journals (Sweden)
Valentin Alexander Schriever
Full Text Available The objective of the present study was to investigate the usefulness of time-frequency analysis (TFA of olfactory-induced EEG change with a low-cost, portable olfactometer in the clinical investigation of smell function.A total of 78 volunteers participated. The study was composed of three parts where olfactory stimuli were presented using a custom-built olfactometer. Part I was designed to optimize the stimulus as well as the recording conditions. In part II EEG-power changes after olfactory/trigeminal stimulation were compared between healthy participants and patients with olfactory impairment. In Part III the test-retest reliability of the method was evaluated in healthy subjects.Part I indicated that the most effective paradigm for stimulus presentation was cued stimulus, with an interstimulus interval of 18-20s at a stimulus duration of 1000ms with each stimulus quality presented 60 times in blocks of 20 stimuli each. In Part II we found that central processing of olfactory stimuli analyzed by TFA differed significantly between healthy controls and patients even when controlling for age. It was possible to reliably distinguish patients with olfactory impairment from healthy individuals at a high degree of accuracy (healthy controls vs anosmic patients: sensitivity 75%; specificity 89%. In addition we could show a good test-retest reliability of TFA of chemosensory induced EEG-power changes in Part III.Central processing of olfactory stimuli analyzed by TFA reliably distinguishes patients with olfactory impairment from healthy individuals at a high degree of accuracy. Importantly this can be achieved with a simple olfactometer.
MEG and EEG data analysis with MNE-Python
Directory of Open Access Journals (Sweden)
Alexandre eGramfort
Full Text Available Magnetoencephalography and electroencephalography (M/EEG measure the weakelectromagnetic signals generated by neuronal activity in the brain. Using thesesignals to characterize and locate neural activation in the brain is achallenge that requires expertise in physics, signalprocessing, statistics, and numerical methods. As part of the MNE softwaresuite, MNE-Python is an open-sourcesoftware package that addresses this challenge by providingstate-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation offunctional connectivity between distributed brain regions.All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysispipelines by writing Python scripts.Moreover, MNE-Python is tightly integrated with the core Python libraries for scientificcomptutation (Numpy, Scipy and visualization (matplotlib and Mayavi, as wellas the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD licenseallowing code reuse, even in commercial products. Although MNE-Python has onlybeen under heavy development for a couple of years, it has rapidly evolved withexpanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices.MNE-Python also gives easy access to preprocessed datasets,helping users to get started quickly and facilitating reproducibility ofmethods by other researchers. Full documentation, including dozens ofexamples, is available at http://martinos.org/mne.
Continuous EEG signal analysis for asynchronous BCI application.
Hsu, Wei-Yen
In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.
Wavelet based analysis of multi-electrode EEG-signals in epilepsy
Hein, Daniel A.; Tetzlaff, Ronald
For many epilepsy patients seizures cannot sufficiently be controlled by an antiepileptic pharmacatherapy. Furthermore, only in small number of cases a surgical treatment may be possible. The aim of this work is to contribute to the realization of an implantable seizure warning device. By using recordings of electroenzephalographical(EEG) signals obtained from the department of epileptology of the University of Bonn we studied a recently proposed algorithm for the detection of parameter changes in nonlinear systems. Firstly, after calculating the crosscorrelation function between the signals of two electrodes near the epileptic focus, a wavelet-analysis follows using a sliding window with the so called Mexican-Hat wavelet. Then the Shannon-Entropy of the wavelet-transformed data has been determined providing the information content on a time scale in subject to the dilation of the wavelet-transformation. It shows distinct changes at the seizure onset for all dilations and for all patients.
EEG-informed fMRI analysis during a hand grip task: estimating the relationship between EEG rhythms and the BOLD signal
Directory of Open Access Journals (Sweden)
Roberta eSclocco
Full Text Available In the last decade, an increasing interest has arisen in investigating the relationship between the electrophysiological and hemodynamic measurements of brain activity, such as EEG and (BOLD fMRI. In particular, changes in BOLD have been shown to be associated with changes in the spectral profile of neural activity, rather than with absolute power. Concurrently, recent findings showed that different EEG rhythms are independently related to changes in the BOLD signal: therefore, it would be important to distinguish between the contributions of the different EEG rhythms to BOLD fluctuations when modeling the relationship between the two signals. Here we propose a method to perform EEG-informed fMRI analysis, in which the EEG regressors take into account both the changes in the spectral profile and the rhythms distinction. We applied it to EEG-fMRI data during a hand grip task in healthy subjects, and compared the results with those obtained by two existing models found in literature. Our results showed that the proposed method better captures the correlations between BOLD signal and EEG rhythms modulations, identifying task-related, well localized activated volumes. Furthermore, we showed that including among the regressors also EEG rhythms not primarily involved in the task enhances the performance of the analysis, even when only correlations with BOLD signal and specific EEG rhythms are explored.
EEG Signal Decomposition and Improved Spectral Analysis Using Wavelet Transform
National Research Council Canada - National Science Library
Bhatti, Muhammad
EEG (Electroencephalograph), as a noninvasive testing method, plays a key role in the diagnosing diseases, and is useful for both physiological research and medical applications. Wavelet transform (WT...
EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.
Wang, Lei; Long, Xi; Arends, Johan B A M; Aarts, Ronald M
The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FD t /h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FD t /h of 1.4s). The proposed VGS-based features can help improve seizure detection for ID patients. Copyright © 2017 Elsevier B.V. All rights reserved.
MEG and EEG data analysis with MNE-Python.
Gramfort, Alexandre; Luessi, Martin; Larson, Eric; Engemann, Denis A; Strohmeier, Daniel; Brodbeck, Christian; Goj, Roman; Jas, Mainak; Brooks, Teon; Parkkonen, Lauri; Hämäläinen, Matti
Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.
High-density EEG coherence analysis using functional units applied to mental fatigue
Caat, Michael ten; Lorist, Monicque M.; Bezdan, Eniko; Roerdink, Jos B.T.M.; Maurits, Natasha M.
Electroencephalography (EEG) coherence provides a quantitative measure of functional brain connectivity which is calculated between pairs of signals as a function of frequency. Without hypotheses, traditional coherence analysis would be cumbersome for high-density EEG which employs a large number of
Convolutive ICA for Spatio-Temporal Analysis of EEG
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai
in the convolutive model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving an EEG ICA subspace. Initial results suggest that in some cases convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model....
Analysis of Small Muscle Movement Effects on EEG Signals
different conditions are recorded in this experiment. These conditions are the resting state, left finger keyboard press, right finger keyboard...51 4.3.2. Right and Left Finger Keyboard Press Conditions ..................................... 57 4.4. Detection of Hand...solving Gamma 30 Hz and higher Blending of multiple brain functions ; Muscle related artifacts 2.2. EEG Artifacts EEG recordings are intended to
Brain Network Analysis from High-Resolution EEG Signals
de Vico Fallani, Fabrizio; Babiloni, Fabio
lattice and a random structure. Such a model has been designated as "small-world" network in analogy with the concept of the small-world phenomenon observed more than 30 years ago in social systems. In a similar way, many types of functional brain networks have been analyzed according to this mathematical approach. In particular, several studies based on different imaging techniques (fMRI, MEG and EEG) have found that the estimated functional networks showed small-world characteristics. In the functional brain connectivity context, these properties have been demonstrated to reflect an optimal architecture for the information processing and propagation among the involved cerebral structures. However, the performance of cognitive and motor tasks as well as the presence of neural diseases has been demonstrated to affect such a small-world topology, as revealed by the significant changes of L and C. Moreover, some functional brain networks have been mostly found to be very unlike the random graphs in their degree-distribution, which gives information about the allocation of the functional links within the connectivity pattern. It was demonstrated that the degree distributions of these networks follow a power-law trend. For this reason those networks are called "scale-free". They still exhibit the small-world phenomenon but tend to contain few nodes that act as highly connected "hubs". Scale-free networks are known to show resistance to failure, facility of synchronization and fast signal processing. Hence, it would be important to see whether the scaling properties of the functional brain networks are altered under various pathologies or experimental tasks. The present Chapter proposes a theoretical graph approach in order to evaluate the functional connectivity patterns obtained from high-resolution EEG signals. In this way, the "Brain Network Analysis" (in analogy with the Social Network Analysis that has emerged as a key technique in modern sociology) represents an
Information-Theoretical Analysis of EEG Microstate Sequences in Python
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Frederic von Wegner
Full Text Available We present an open-source Python package to compute information-theoretical quantities for electroencephalographic data. Electroencephalography (EEG measures the electrical potential generated by the cerebral cortex and the set of spatial patterns projected by the brain's electrical potential on the scalp surface can be clustered into a set of representative maps called EEG microstates. Microstate time series are obtained by competitively fitting the microstate maps back into the EEG data set, i.e., by substituting the EEG data at a given time with the label of the microstate that has the highest similarity with the actual EEG topography. As microstate sequences consist of non-metric random variables, e.g., the letters A–D, we recently introduced information-theoretical measures to quantify these time series. In wakeful resting state EEG recordings, we found new characteristics of microstate sequences such as periodicities related to EEG frequency bands. The algorithms used are here provided as an open-source package and their use is explained in a tutorial style. The package is self-contained and the programming style is procedural, focusing on code intelligibility and easy portability. Using a sample EEG file, we demonstrate how to perform EEG microstate segmentation using the modified K-means approach, and how to compute and visualize the recently introduced information-theoretical tests and quantities. The time-lagged mutual information function is derived as a discrete symbolic alternative to the autocorrelation function for metric time series and confidence intervals are computed from Markov chain surrogate data. The software package provides an open-source extension to the existing implementations of the microstate transform and is specifically designed to analyze resting state EEG recordings.
Nonlinear analysis of dynamic signature
Rashidi, S.; Fallah, A.; Towhidkhah, F.
Signature is a long trained motor skill resulting in well combination of segments like strokes and loops. It is a physical manifestation of complex motor processes. The problem, generally stated, is that how relative simplicity in behavior emerges from considerable complexity of perception-action system that produces behavior within an infinitely variable biomechanical and environmental context. To solve this problem, we present evidences which indicate that motor control dynamic in signing process is a chaotic process. This chaotic dynamic may explain a richer array of time series behavior in motor skill of signature. Nonlinear analysis is a powerful approach and suitable tool which seeks for characterizing dynamical systems through concepts such as fractal dimension and Lyapunov exponent. As a result, they can be analyzed in both horizontal and vertical for time series of position and velocity. We observed from the results that noninteger values for the correlation dimension indicates low dimensional deterministic dynamics. This result could be confirmed by using surrogate data tests. We have also used time series to calculate the largest Lyapunov exponent and obtain a positive value. These results constitute significant evidence that signature data are outcome of chaos in a nonlinear dynamical system of motor control.
Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification
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Shengkun Xie
Full Text Available Classification of electroencephalography (EEG is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.
A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies.
Puce, Aina; Hämäläinen, Matti S
Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed.
A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies
Directory of Open Access Journals (Sweden)
Aina Puce
Full Text Available Electroencephalography (EEG and magnetoencephalography (MEG are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed.
Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats.
Ouyang, Gaoxiang; Li, Xiaoli; Dang, Chuangyin; Richards, Douglas A
Understanding the transition of brain activity towards an absence seizure is a challenging task. In this paper, we use recurrence quantification analysis to indicate the deterministic dynamics of EEG series at the seizure-free, pre-seizure and seizure states in genetic absence epilepsy rats. The determinism measure, DET, based on recurrence plot, was applied to analyse these three EEG datasets, each dataset containing 300 single-channel EEG epochs of 5-s duration. Then, statistical analysis of the DET values in each dataset was carried out to determine whether their distributions over the three groups were significantly different. Furthermore, a surrogate technique was applied to calculate the significance level of determinism measures in EEG recordings. The mean (+/-SD) DET of EEG was 0.177+/-0.045 in pre-seizure intervals. The DET values of pre-seizure EEG data are significantly higher than those of seizure-free intervals, 0.123+/-0.023, (Pdeterminism in EEG epochs was present in 25 of 300 (8.3%), 181 of 300 (60.3%) and 289 of 300 (96.3%) in seizure-free, pre-seizure and seizure intervals, respectively. Results provide some first indications that EEG epochs during pre-seizure intervals exhibit a higher degree of determinism than seizure-free EEG epochs, but lower than those in seizure EEG epochs in absence epilepsy. The proposed methods have the potential of detecting the transition between normal brain activity and the absence seizure state, thus opening up the possibility of intervention, whether electrical or pharmacological, to prevent the oncoming seizure.
Combining SPECT and Quantitative EEG Analysis for the Automated Differential Diagnosis of Disorders with Amnestic Symptoms
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Yvonne Höller
Full Text Available Single photon emission computed tomography (SPECT and Electroencephalography (EEG have become established tools in routine diagnostics of dementia. We aimed to increase the diagnostic power by combining quantitative markers from SPECT and EEG for differential diagnosis of disorders with amnestic symptoms. We hypothesize that the combination of SPECT with measures of interaction (connectivity in the EEG yields higher diagnostic accuracy than the single modalities. We examined 39 patients with Alzheimer's dementia (AD, 69 patients with depressive cognitive impairment (DCI, 71 patients with amnestic mild cognitive impairment (aMCI, and 41 patients with amnestic subjective cognitive complaints (aSCC. We calculated 14 measures of interaction from a standard clinical EEG-recording and derived graph-theoretic network measures. From regional brain perfusion measured by 99mTc-hexamethyl-propylene-aminoxime (HMPAO-SPECT in 46 regions, we calculated relative cerebral perfusion in these patients. Patient groups were classified pairwise with a linear support vector machine. Classification was conducted separately for each biomarker, and then again for each EEG- biomarker combined with SPECT. Combination of SPECT with EEG-biomarkers outperformed single use of SPECT or EEG when classifying aSCC vs. AD (90%, aMCI vs. AD (70%, and AD vs. DCI (100%, while a selection of EEG measures performed best when classifying aSCC vs. aMCI (82% and aMCI vs. DCI (90%. Only the contrast between aSCC and DCI did not result in above-chance classification accuracy (60%. In general, accuracies were higher when measures of interaction (i.e., connectivity measures were applied directly than when graph-theoretical measures were derived. We suggest that quantitative analysis of EEG and machine-learning techniques can support differentiating AD, aMCI, aSCC, and DCC, especially when being combined with imaging methods such as SPECT. Quantitative analysis of EEG connectivity could become
Semi-automated analysis of EEG spikes in the preterm fetal sheep using wavelet analysis
International Nuclear Information System (INIS)
Walbran, A.C.; Unsworth, C.P.; Gunn, A.J.; Benett, L.
Full text: Presentation Preference Oral Presentation Perinatal hypoxia plays a key role in the cause of brain injury in premature infants. Cerebral hypothermia commenced in the latent phase of evolving injury (first 6-8 h post hypoxic-ischemic insult) is the lead candidate for treatment however currently there is no means to identify which infants can benefit from treatment. Recent studies suggest that epileptiform transients in latent phase are predictive of neural outcome. To quantify this, an automated means of EEG analysis is required as EEG monitoring produces vast amounts of data which is timely to analyse manually. We have developed a semi-automated EEG spike detection method which employs a discretized version of the continuous wavelet transform (CWT). EEG data was obtained from a fetal sheep at approximately 0.7 of gestation. Fetal asphyxia was maintained for 25 min and the EEG recorded for 8 h before and after asphyxia. The CWT was calculated followed by the power of the wavelet transform coefficients. Areas of high power corresponded to spike waves so thresholding was employed to identify the spikes. The performance of the method was found have a good sensitivity and selectivity, thus demonstrating that this method is a simple, robust and potentially effective spike detection algorithm.
Bayesian Correlated Component Analysis for inference of joint EEG activation
DEFF Research Database (Denmark)
Poulsen, Andreas Trier; Kamronn, Simon Due; Parra, Lucas
We propose a probabilistic generative multi-view model to test the representational universality of human information processing. The model is tested in simulated data and in a well-established benchmark EEG dataset.......We propose a probabilistic generative multi-view model to test the representational universality of human information processing. The model is tested in simulated data and in a well-established benchmark EEG dataset....
Review on solving the inverse problem in EEG source analysis
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Fabri Simon G
Full Text Available Abstract In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF, SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF
CUDAICA: GPU Optimization of Infomax-ICA EEG Analysis
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Federico Raimondo
Full Text Available In recent years, Independent Component Analysis (ICA has become a standard to identify relevant dimensions of the data in neuroscience. ICA is a very reliable method to analyze data but it is, computationally, very costly. The use of ICA for online analysis of the data, used in brain computing interfaces, results are almost completely prohibitive. We show an increase with almost no cost (a rapid video card of speed of ICA by about 25 fold. The EEG data, which is a repetition of many independent signals in multiple channels, is very suitable for processing using the vector processors included in the graphical units. We profiled the implementation of this algorithm and detected two main types of operations responsible of the processing bottleneck and taking almost 80% of computing time: vector-matrix and matrix-matrix multiplications. By replacing function calls to basic linear algebra functions to the standard CUBLAS routines provided by GPU manufacturers, it does not increase performance due to CUDA kernel launch overhead. Instead, we developed a GPU-based solution that, comparing with the original BLAS and CUBLAS versions, obtains a 25x increase of performance for the ICA calculation.
Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference
International Nuclear Information System (INIS)
Xu, Peng; Xiong, Xiu Chun; Tian, Yin; Zhang, Rui; Li, Pei Yang; Yao, De Zhong; Xue, Qing; Wang, Yu Ping; Peng, Yueheng
The diagnosis of mild cognitive impairment (MCI) is very helpful for early therapeutic interventions of Alzheimer's disease (AD). MCI has been proven to be correlated with disorders in multiple brain areas. In this paper, we used information from resting brain networks at different EEG frequency bands to reliably recognize MCI. Because EEG network analysis is influenced by the reference that is used, we also evaluate the effect of the reference choices on the resting scalp EEG network-based MCI differentiation. The conducted study reveals two aspects: (1) the network-based MCI differentiation is superior to the previously reported classification that uses coherence in the EEG; and (2) the used EEG reference influences the differentiation performance, and the zero approximation technique (reference electrode standardization technique, REST) can construct a more accurate scalp EEG network, which results in a higher differentiation accuracy for MCI. This study indicates that the resting scalp EEG-based network analysis could be valuable for MCI recognition in the future. (paper)
Resonance detection of EEG signals using two-layer wavelet analysis
International Nuclear Information System (INIS)
Abdallah, H. M; Odeh, F.S.
This paper presents the hybrid quadrature mirror filter (HQMF) algorithm applied to the electroencephalogram (EEG) signal during mental activity. The information contents of this signal, i.e., its medical diagnosis, lie in its power spectral density (PSD). The HQMF algorithm is a modified technique that is based on the shape and the details of the signal. If applied efficiently, the HQMF algorithm will produce much better results than conventional wavelet methods in detecting (diagnosing) the information of the EEG signal from its PSD. This technique is applicable not only to EEG signals, but is highly recommended to compression analysis and de noising techniques. (authors). 16 refs., 9 figs
Introduction to nonlinear finite element analysis
Kim, Nam-Ho
This book introduces the key concepts of nonlinear finite element analysis procedures. The book explains the fundamental theories of the field and provides instructions on how to apply the concepts to solving practical engineering problems. Instead of covering many nonlinear problems, the book focuses on three representative problems: nonlinear elasticity, elastoplasticity, and contact problems. The book is written independent of any particular software, but tutorials and examples using four commercial programs are included as appendices: ANSYS, NASTRAN, ABAQUS, and MATLAB. In particular, the MATLAB program includes all source codes so that students can develop their own material models, or different algorithms. This book also: · Presents clear explanations of nonlinear finite element analysis for elasticity, elastoplasticity, and contact problems · Includes many informative examples of nonlinear analyses so that students can clearly understand the nonlinear theory · ...
Spectral theory and nonlinear functional analysis
Lopez-Gomez, Julian
This Research Note addresses several pivotal problems in spectral theory and nonlinear functional analysis in connection with the analysis of the structure of the set of zeroes of a general class of nonlinear operators. It features the construction of an optimal algebraic/analytic invariant for calculating the Leray-Schauder degree, new methods for solving nonlinear equations in Banach spaces, and general properties of components of solutions sets presented with minimal use of topological tools. The author also gives several applications of the abstract theory to reaction diffusion equations and systems.The results presented cover a thirty-year period and include recent, unpublished findings of the author and his coworkers. Appealing to a broad audience, Spectral Theory and Nonlinear Functional Analysis contains many important contributions to linear algebra, linear and nonlinear functional analysis, and topology and opens the door for further advances.
Method for nonlinear exponential regression analysis
Junkin, B. G.
Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.
A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis
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Balbir Singh
Full Text Available EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA, which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of "dictionary." MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies.
A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis
Wagatsuma, Hiroaki
EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of "dictionary." MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221
Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis.
Somers, Ben; Bertrand, Alexander
Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.
Dynamics and vibrations progress in nonlinear analysis
Kachapi, Seyed Habibollah Hashemi
Dynamical and vibratory systems are basically an application of mathematics and applied sciences to the solution of real world problems. Before being able to solve real world problems, it is necessary to carefully study dynamical and vibratory systems and solve all available problems in case of linear and nonlinear equations using analytical and numerical methods. It is of great importance to study nonlinearity in dynamics and vibration; because almost all applied processes act nonlinearly, and on the other hand, nonlinear analysis of complex systems is one of the most important and complicated tasks, especially in engineering and applied sciences problems. There are probably a handful of books on nonlinear dynamics and vibrations analysis. Some of these books are written at a fundamental level that may not meet ambitious engineering program requirements. Others are specialized in certain fields of oscillatory systems, including modeling and simulations. In this book, we attempt to strike a balance between th...
Attenuation of artifacts in EEG signals measured inside an MRI scanner using constrained independent component analysis
International Nuclear Information System (INIS)
Rasheed, Tahir; Lee, Young-Koo; Lee, Soo Yeol; Kim, Tae-Seong
Integration of electroencephalography (EEG) and functional magnetic imaging (fMRI) resonance will allow analysis of the brain activities at superior temporal and spatial resolution. However simultaneous acquisition of EEG and fMRI is hindered by the enhancement of artifacts in EEG, the most prominent of which are ballistocardiogram (BCG) and electro-oculogram (EOG) artifacts. The situation gets even worse if the evoked potentials are measured inside MRI for their minute responses in comparison to the spontaneous brain responses. In this study, we propose a new method of attenuating these artifacts from the spontaneous and evoked EEG data acquired inside an MRI scanner using constrained independent component analysis with a priori information about the artifacts as constraints. With the proposed techniques of reference function generation for the BCG and EOG artifacts as constraints, our new approach performs significantly better than the averaged artifact subtraction (AAS) method. The proposed method could be an alternative to the conventional ICA method for artifact attenuation, with some advantages. As a performance measure we have achieved much improved normalized power spectrum ratios (INPS) for continuous EEG and correlation coefficient (cc) values with outside MRI visual evoked potentials for visual evoked EEG, as compared to those obtained with the AAS method. The results show that our new approach is more effective than the conventional methods, almost fully automatic, and no extra ECG signal measurements are involved
Acute toxicity and sleep-wake EEG analysis of Stachtarpheta ...
African Journals Online (AJOL)
The effect of systemic administration of TASC on sleep architecture in rats was also evaluated in Sprague-Dawley rats that were chronically implanted with electrodes for electroencephalogram (EEG) and electromyogram (EMG) recording. The acute toxicity test revealed no lethal effect with doses of SCCR (up to 2000 ...
Analysis of tractable distortion metrics for EEG compression applications
International Nuclear Information System (INIS)
Bazán-Prieto, Carlos; Blanco-Velasco, Manuel; Cruz-Roldán, Fernando; Cárdenas-Barrera, Julián
Coding distortion in lossy electroencephalographic (EEG) signal compression methods is evaluated through tractable objective criteria. The percentage root-mean-square difference, which is a global and relative indicator of the quality held by reconstructed waveforms, is the most widely used criterion. However, this parameter does not ensure compliance with clinical standard guidelines that specify limits to allowable noise in EEG recordings. As a result, expert clinicians may have difficulties interpreting the resulting distortion of the EEG for a given value of this parameter. Conversely, the root-mean-square error is an alternative criterion that quantifies distortion in understandable units. In this paper, we demonstrate that the root-mean-square error is better suited to control and to assess the distortion introduced by compression methods. The experiments conducted in this paper show that the use of the root-mean-square error as target parameter in EEG compression allows both clinicians and scientists to infer whether coding error is clinically acceptable or not at no cost for the compression ratio. (paper)
Analysis of Nonlinear Dynamic Structures
African Journals Online (AJOL)
work a two degrees of freedom nonlinear system with zero memory was ... FRF is the most widely used method in structural dynamics which gives information about the ..... 3.6, which is the waterfall diagram of the same response, as well.
Early EEG for outcome prediction of postanoxic coma: prospective cohort study with cost-minimization analysis.
Sondag, Lotte; Ruijter, Barry J; Tjepkema-Cloostermans, Marleen C; Beishuizen, Albertus; Bosch, Frank H; van Til, Janine A; van Putten, Michel J A M; Hofmeijer, Jeannette
We recently showed that electroencephalography (EEG) patterns within the first 24 hours robustly contribute to multimodal prediction of poor or good neurological outcome of comatose patients after cardiac arrest. Here, we confirm these results and present a cost-minimization analysis. Early prognosis contributes to communication between doctors and family, and may prevent inappropriate treatment. A prospective cohort study including 430 subsequent comatose patients after cardiac arrest was conducted at intensive care units of two teaching hospitals. Continuous EEG was started within 12 hours after cardiac arrest and continued up to 3 days. EEG patterns were visually classified as unfavorable (isoelectric, low-voltage, or burst suppression with identical bursts) or favorable (continuous patterns) at 12 and 24 hours after cardiac arrest. Outcome at 6 months was classified as good (cerebral performance category (CPC) 1 or 2) or poor (CPC 3, 4, or 5). Predictive values of EEG measures and cost-consequences from a hospital perspective were investigated, assuming EEG-based decision- making about withdrawal of life-sustaining treatment in the case of a poor predicted outcome. Poor outcome occurred in 197 patients (51% of those included in the analyses). Unfavorable EEG patterns at 24 hours predicted a poor outcome with specificity of 100% (95% CI 98-100%) and sensitivity of 29% (95% CI 22-36%). Favorable patterns at 12 hours predicted good outcome with specificity of 88% (95% CI 81-93%) and sensitivity of 51% (95% CI 42-60%). Treatment withdrawal based on an unfavorable EEG pattern at 24 hours resulted in a reduced mean ICU length of stay without increased mortality in the long term. This gave small cost reductions, depending on the timing of withdrawal. Early EEG contributes to reliable prediction of good or poor outcome of postanoxic coma and may lead to reduced length of ICU stay. In turn, this may bring small cost reductions.
Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal
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Shanzhi Xu
Full Text Available The recorded electroencephalography (EEG signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.
Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal.
Xu, Shanzhi; Hu, Hai; Ji, Linhong; Wang, Peng
The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC) and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA) EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.
Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements.
Muthuraman, Muthuraman; Hellriegel, Helge; Hoogenboom, Nienke; Anwar, Abdul Rauf; Mideksa, Kidist Gebremariam; Krause, Holger; Schnitzler, Alfons; Deuschl, Günther; Raethjen, Jan
Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2-4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.
Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements.
Directory of Open Access Journals (Sweden)
Muthuraman Muthuraman
Full Text Available Electroencephalography (EEG and magnetoencephalography (MEG are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2-4 Hz and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC. MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.
Characterizing Alzheimer's disease severity via resting-awake EEG amplitude modulation analysis.
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Francisco J Fraga
Full Text Available Changes in electroencephalography (EEG amplitude modulations have recently been linked with early-stage Alzheimer's disease (AD. Existing tools available to perform such analysis (e.g., detrended fluctuation analysis, however, provide limited gains in discriminability power over traditional spectral based EEG analysis. In this paper, we explore the use of an innovative EEG amplitude modulation analysis technique based on spectro-temporal signal processing. More specifically, full-band EEG signals are first decomposed into the five well-known frequency bands and the envelopes are then extracted via a Hilbert transform. Each of the five envelopes are further decomposed into four so-called modulation bands, which were chosen to coincide with the delta, theta, alpha and beta frequency bands. Experiments on a resting-awake EEG dataset collected from 76 participants (27 healthy controls, 27 diagnosed with mild-AD, and 22 with moderate-AD showed significant differences in amplitude modulations between the three groups. Most notably, i delta modulation of the beta frequency band disappeared with an increase in disease severity (from mild to moderate AD, ii delta modulation of the theta band appeared with an increase in severity, and iii delta modulation of the beta frequency band showed to be a reliable discriminant feature between healthy controls and mild-AD patients. Taken together, it is hoped that the developed tool can be used to assist clinicians not only with early detection of Alzheimer's disease, but also to monitor its progression.
Preparing laboratory and real-world EEG data for large-scale analysis: A containerized approach
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Nima eBigdely-Shamlo
Full Text Available Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface (BCI models.. However, the absence of standard-ized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the diffi-culty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a containerized approach and freely available tools we have developed to facilitate the process of an-notating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-analysis. The EEG Study Schema (ESS comprises three data Levels, each with its own XML-document schema and file/folder convention, plus a standardized (PREP pipeline to move raw (Data Level 1 data to a basic preprocessed state (Data Level 2 suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are in-creasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at eegstudy.org, and a central cata-log of over 850 GB of existing data in ESS format is available at study-catalog.org. These tools and resources are part of a larger effort to ena-ble data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org.
Review on solving the forward problem in EEG source analysis
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Vergult Anneleen
Full Text Available Abstract Background The aim of electroencephalogram (EEG source localization is to find the brain areas responsible for EEG waves of interest. It consists of solving forward and inverse problems. The forward problem is solved by starting from a given electrical source and calculating the potentials at the electrodes. These evaluations are necessary to solve the inverse problem which is defined as finding brain sources which are responsible for the measured potentials at the EEG electrodes. Methods While other reviews give an extensive summary of the both forward and inverse problem, this review article focuses on different aspects of solving the forward problem and it is intended for newcomers in this research field. Results It starts with focusing on the generators of the EEG: the post-synaptic potentials in the apical dendrites of pyramidal neurons. These cells generate an extracellular current which can be modeled by Poisson's differential equation, and Neumann and Dirichlet boundary conditions. The compartments in which these currents flow can be anisotropic (e.g. skull and white matter. In a three-shell spherical head model an analytical expression exists to solve the forward problem. During the last two decades researchers have tried to solve Poisson's equation in a realistically shaped head model obtained from 3D medical images, which requires numerical methods. The following methods are compared with each other: the boundary element method (BEM, the finite element method (FEM and the finite difference method (FDM. In the last two methods anisotropic conducting compartments can conveniently be introduced. Then the focus will be set on the use of reciprocity in EEG source localization. It is introduced to speed up the forward calculations which are here performed for each electrode position rather than for each dipole position. Solving Poisson's equation utilizing FEM and FDM corresponds to solving a large sparse linear system. Iterative
Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis
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Yuan-Pin Lin
Full Text Available Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability, arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18 and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40% as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing neither achieved the cross-day classification (the accuracy was around chance level nor replicated the encouraging improvement due to the inter-day EEG variability. This result
Seismic analysis of piping with nonlinear supports
International Nuclear Information System (INIS)
Barta, D.A.; Huang, S.N.; Severud, L.K.
The modeling and results of nonlinear time-history seismic analyses for three sizes of pipelines restrained by mechanical snubbes are presented. Numerous parametric analyses were conducted to obtain sensitivity information which identifies relative importance of the model and analysis ingredients. Special considerations for modeling the pipe clamps and the mechanical snubbers based on experimental characterization data are discussed. Comparisions are also given of seismic responses, loads and pipe stresses predicted by standard response spectra methods and the nonlinear time-history methods
Dynamic nonlinear analysis of shells of revolution
International Nuclear Information System (INIS)
Riesemann, W.A. von; Stricklin, J.A.; Haisler, W.E.
Over the past few years a series of finite element computer programs have been developed at Texas A and M University for the static and dynamic nonlinear analysis of shells of revolution. This paper discusses one of these, DYNAPLAS, which is a program for the transient response of ring stiffened shells of revolution subjected to either asymmetric initial velocities or to asymmetric pressure loadings. Both material and geometric nonlinearities may be considered. (Auth.)
Prediction of advertisement preference by fusing EEG response and sentiment analysis.
Gauba, Himaanshu; Kumar, Pradeep; Roy, Partha Pratim; Singh, Priyanka; Dogra, Debi Prosad; Raman, Balasubramanian
This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet. We have fused Electroencephalogram (EEG) waves of user and corresponding global textual comments of the video to understand the user's preference more precisely. In our framework, the users were asked to watch the video-advertisement and simultaneously EEG signals were recorded. Valence scores were obtained using self-report for each video. A higher valence corresponds to intrinsic attractiveness of the user. Furthermore, the multimedia data that comprised of the comments posted by global viewers, were retrieved and processed using Natural Language Processing (NLP) technique for sentiment analysis. Textual contents from review comments were analyzed to obtain a score to understand sentiment nature of the video. A regression technique based on Random forest was used to predict the rating of an advertisement using EEG data. Finally, EEG based rating is combined with NLP-based sentiment score to improve the overall prediction. The study was carried out using 15 video clips of advertisements available online. Twenty five participants were involved in our study to analyze our proposed system. The results are encouraging and these suggest that the proposed multimodal approach can achieve lower RMSE in rating prediction as compared to the prediction using only EEG data. Copyright © 2017 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Plamen D. Dimitrov
Full Text Available Background: Autism is a mental developmental disorder, manifested in the early childhood. Attention deficit hyperactivity disorder is another psychiatric condition of the neurodevelopmental type. Both disorders affect information processing in the nervous system, altering the mechanisms which control how neurons and their synapses are connected and organized. Purpose: To examine if quantitative EEG assessment is sensitive and simple enough to differentiate autism from attention deficit hyperactivity disorder and neurologically typical children. Material and methods: Quantitative EEG is a type of electrophysiological assessment that uses computerized mathematical analysis to convert the raw waveform data into different frequency ranges. Each frequency range is averaged across a sample of data and quantified into mean amplitude (voltage in microvolts mV. We performed quantitative EEG analysis and compared 4 cohorts of children (aged from 3 to 7 years: with autism (high [n=27] and low [n=52] functioning, with attention deficit hyperactivity disorder [n=34], and with typical behavior [n75]. Results: Our preliminary results show that there are significant qEEG differences between the groups of patients and the control cohort. The changes affect the potential levels of delta-, theta-, alpha-, and beta- frequency spectrums. Conclusion: The present study shows some significant quantitative EEG findings in autistic patients. This is a step forward in our efforts, aimed at defining specific neurophysiologic changes, in order to develop and refine strategies for early diagnosis of autism spectrum disorders, differentiation from other development conditions in childhood, detection of specific biomarkers and early initiation of treatment.
Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering
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Chin-Teng Lin
Full Text Available Electroencephalogram (EEG signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA, feature extraction, and the Gaussian mixture model (GMM to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.
Independent component analysis of gait-related movement artifact recorded using EEG electrodes during treadmill walking.
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Kristine Lynne Snyder
Full Text Available There has been a recent surge in the use of electroencephalography (EEG as a tool for mobile brain imaging due to its portability and fine time resolution. When EEG is combined with independent component analysis (ICA and source localization techniques, it can model electrocortical activity as arising from temporally independent signals located in spatially distinct cortical areas. However, for mobile tasks, it is not clear how movement artifacts influence ICA and source localization. We devised a novel method to collect pure movement artifact data (devoid of any electrophysiological signals with a 256-channel EEG system. We first blocked true electrocortical activity using a silicone swim cap. Over the silicone layer, we placed a simulated scalp with electrical properties similar to real human scalp. We collected EEG movement artifact signals from ten healthy, young subjects wearing this setup as they walked on a treadmill at speeds from 0.4-1.6 m/s. We performed ICA and dipole fitting on the EEG movement artifact data to quantify how accurately these methods would identify the artifact signals as non-neural. ICA and dipole fitting accurately localized 99% of the independent components in non-neural locations or lacked dipolar characteristics. The remaining 1% of sources had locations within the brain volume and low residual variances, but had topographical maps, power spectra, time courses, and event related spectral perturbations typical of non-neural sources. Caution should be exercised when interpreting ICA for data that includes semi-periodic artifacts including artifact arising from human walking. Alternative methods are needed for the identification and separation of movement artifact in mobile EEG signals, especially methods that can be performed in real time. Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.
Resting State EEG in Children With Learning Disabilities: An Independent Component Analysis Approach.
Jäncke, Lutz; Alahmadi, Nsreen
In this study, the neurophysiological underpinnings of learning disabilities (LD) in children are examined using resting state EEG. We were particularly interested in the neurophysiological differences between children with learning disabilities not otherwise specified (LD-NOS), learning disabilities with verbal disabilities (LD-Verbal), and healthy control (HC) children. We applied 2 different approaches to examine the differences between the different groups. First, we calculated theta/beta and theta/alpha ratios in order to quantify the relationship between slow and fast EEG oscillations. Second, we used a recently developed method for analyzing spectral EEG, namely the group independent component analysis (gICA) model. Using these measures, we identified substantial differences between LD and HC children and between LD-NOS and LD-Verbal children in terms of their spectral EEG profiles. We obtained the following findings: (a) theta/beta and theta/alpha ratios were substantially larger in LD than in HC children, with no difference between LD-NOS and LD-Verbal children; (b) there was substantial slowing of EEG oscillations, especially for gICs located in frontal scalp positions, with LD-NOS children demonstrating the strongest slowing; (c) the estimated intracortical sources of these gICs were mostly located in brain areas involved in the control of executive functions, attention, planning, and language; and (d) the LD-Verbal children demonstrated substantial differences in EEG oscillations compared with LD-NOS children, and these differences were localized in language-related brain areas. The general pattern of atypical neurophysiological activation found in LD children suggests that they suffer from neurophysiological dysfunction in brain areas involved with the control of attention, executive functions, planning, and language functions. LD-Verbal children also demonstrate atypical activation, especially in language-related brain areas. These atypical
A Quantitative Analysis of an EEG Epileptic Record Based on MultiresolutionWavelet Coefficients
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Mariel Rosenblatt
Full Text Available The characterization of the dynamics associated with electroencephalogram (EEG signal combining an orthogonal discrete wavelet transform analysis with quantifiers originated from information theory is reviewed. In addition, an extension of this methodology based on multiresolution quantities, called wavelet leaders, is presented. In particular, the temporal evolution of Shannon entropy and the statistical complexity evaluated with different sets of multiresolution wavelet coefficients are considered. Both methodologies are applied to the quantitative EEG time series analysis of a tonic-clonic epileptic seizure, and comparative results are presented. In particular, even when both methods describe the dynamical changes of the EEG time series, the one based on wavelet leaders presents a better time resolution.
Electrophysiological Measures of Regional Neural Interactive Coupling (Linear and Nonlinear Dependence Relationships Among Multiple Channel Electroencephalographic (EEG) Recordings),
clinical intervention . SG1CUDING CCMENL' In evaluating the EEGs of subjects it is important to not that . ~major differences in EEG waveshape across...studies in dyslexia . In A.L. Benton and D. Pearl (Fs.), Dyslexia : An Appraisal of Current Knowledge. New York: Oxford University Press, 1978. 4...Electroencephalo- graphy and Clinical Neurophysio !. Oct., 67, 23(4):306-19. 6) Duffy, F.H., Denckla, M.B., Bartels, P.H., and Sandini, G. Dyslexia
Independent component analysis separates spikes of different origin in the EEG.
Urrestarazu, Elena; Iriarte, Jorge; Artieda, Julio; Alegre, Manuel; Valencia, Miguel; Viteri, César
Independent component analysis (ICA) is a novel system that finds independent sources in recorded signals. Its usefulness in separating epileptiform activity of different origin has not been determined. The goal of this study was to demonstrate that ICA is useful for separating different spikes using samples of EEG of patients with focal epilepsy. Digital EEG samples from four patients with focal epilepsy were included. The patients had temporal (n = 2), centrotemporal (n = 1) or frontal spikes (n = 1). Twenty-six samples with two (or more) spikes from two different patients were created. The selection of the two spikes for each mixed EEG was performed randomly, trying to have all the different combinations and rejecting the mixture of two spikes from the same patient. Two different examiners studied the EEGs using ICA with JADE paradigm in Matlab platform, trying to separate and to identify the spikes. They agreed in the correct separation of the spikes in 24 of the 26 samples, classifying the spikes as frontal, temporal or centrotemporal, left or right sided. The demonstration of the possibility of detecting different artificially mixed spikes confirms that ICA may be useful in separating spikes or other elements in real EEGs.
EEG Neurofeedback treatments in children with ADHD: An updated meta-analysis of Randomized Controlled Trials
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Jean-Arthur eMicoulaud Franchi
Full Text Available Objective We undertook a meta-analysis of published Randomized Controlled Trials (RCT with semi-active control and sham-NF groups to determine whether EEG-NF significantly improves the overall symptoms, inattention and hyperactivity/impulsivity dimensions for probably unblinded assessment (parent assessment and probably blinded assessment (teacher assessment in children with Attention Deficit Hyperactivity Disorder (ADHD.Data Sources A systematic review identified independent studies that were eligible for inclusion in a random effects meta-analysis.Data Extraction Effect sizes for ADHD symptoms were expressed as standardized mean differences (SMD with 95% confidence intervals.ResultsFive identified studies met eligibility criteria, 263 patients with ADHD were included, 146 patients were trained with EEG-NF. On parent assessment (probably unblinded assessment, the overall ADHD score (SMD=-0.49 [-0.74, -0.24], the inattention score (SMD=-0.46 [-0.76, -0.15] and the hyperactivity/impulsivity score (SMD=-0.34 [-0.59, -0.09] were significantly improved in patients receiving EEG-NF compared to controls. On teacher assessment (probably blinded assessment, only the inattention score was significantly improved in patients receiving EEG-NF compared to controls (SMD=-0.30 [-0.58, -0.03]. ConclusionsThis meta-analysis of EEG-NF in children with ADHD highlights improvement in the inattention dimension of ADHD symptoms. Future investigations should pay greater attention to adequately blinded studies and EEG-NF protocols that carefully control the implementation and embedding of training.
Incorporating priors for EEG source imaging and connectivity analysis
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Xu eLei
Full Text Available Electroencephalography source imaging (ESI is a useful technique to localize the generators from a given scalp electric measurement and to investigate the temporal dynamics of the large-scale neural circuits. By introducing reasonable priors from other modalities, ESI reveals the most probable sources and communication structures at every moment in time. Here, we review the available priors from such techniques as magnetic resonance imaging (MRI, functional MRI (fMRI, and positron emission tomography (PET. The modality's specific contribution is analyzed from the perspective of source reconstruction. For spatial priors, such as EEG-correlated fMRI, temporally coherent networks and resting-state fMRI are systematically introduced in the ESI. Moreover, the fiber tracking (diffusion tensor imaging, DTI and neuro-stimulation techniques (transcranial magnetic stimulation, TMS are also introduced as the potential priors, which can help to draw inferences about the neuroelectric connectivity in the source space. We conclude that combining EEG source imaging with other complementary modalities is a promising approach towards the study of brain networks in cognitive and clinical neurosciences.
Graph theoretical analysis of EEG functional connectivity during music perception.
Wu, Junjie; Zhang, Junsong; Liu, Chu; Liu, Dongwei; Ding, Xiaojun; Zhou, Changle
The present study evaluated the effect of music on large-scale structure of functional brain networks using graph theoretical concepts. While most studies on music perception used Western music as an acoustic stimulus, Guqin music, representative of Eastern music, was selected for this experiment to increase our knowledge of music perception. Electroencephalography (EEG) was recorded from non-musician volunteers in three conditions: Guqin music, noise and silence backgrounds. Phase coherence was calculated in the alpha band and between all pairs of EEG channels to construct correlation matrices. Each resulting matrix was converted into a weighted graph using a threshold, and two network measures: the clustering coefficient and characteristic path length were calculated. Music perception was found to display a higher level mean phase coherence. Over the whole range of thresholds, the clustering coefficient was larger while listening to music, whereas the path length was smaller. Networks in music background still had a shorter characteristic path length even after the correction for differences in mean synchronization level among background conditions. This topological change indicated a more optimal structure under music perception. Thus, prominent small-world properties are confirmed in functional brain networks. Furthermore, music perception shows an increase of functional connectivity and an enhancement of small-world network organizations. Copyright © 2012 Elsevier B.V. All rights reserved.
A topological introduction to nonlinear analysis
Brown, Robert F
This third edition of A Topological Introduction to Nonlinear Analysis is addressed to the mathematician or graduate student of mathematics - or even the well-prepared undergraduate - who would like, with a minimum of background and preparation, to understand some of the beautiful results at the heart of nonlinear analysis. Based on carefully-expounded ideas from several branches of topology, and illustrated by a wealth of figures that attest to the geometric nature of the exposition, the book will be of immense help in providing its readers with an understanding of the mathematics of the nonlinear phenomena that characterize our real world. For this third edition, several new chapters present the fixed point index and its applications. The exposition and mathematical content is improved throughout. This book is ideal for self-study for mathematicians and students interested in such areas of geometric and algebraic topology, functional analysis, differential equations, and applied mathematics. It is a sharply...
Interval analysis of interictal EEG: pathology of the alpha rhythm in focal epilepsy
Pyrzowski, Jan; Siemiński, Mariusz; Sarnowska, Anna; Jedrzejczak, Joanna; Nyka, Walenty M.
The contemporary use of interictal scalp electroencephalography (EEG) in the context of focal epilepsy workup relies on the visual identification of interictal epileptiform discharges. The high-specificity performance of this marker comes, however, at a cost of only moderate sensitivity. Zero-crossing interval analysis is an alternative to Fourier analysis for the assessment of the rhythmic component of EEG signals. We applied this method to standard EEG recordings of 78 patients divided into 4 subgroups: temporal lobe epilepsy (TLE), frontal lobe epilepsy (FLE), psychogenic nonepileptic seizures (PNES) and nonepileptic patients with headache. Interval-analysis based markers were capable of effectively discriminating patients with epilepsy from those in control subgroups (AUC~0.8) with diagnostic sensitivity potentially exceeding that of visual analysis. The identified putative epilepsy-specific markers were sensitive to the properties of the alpha rhythm and displayed weak or non-significant dependences on the number of antiepileptic drugs (AEDs) taken by the patients. Significant AED-related effects were concentrated in the theta interval range and an associated marker allowed for identification of patients on AED polytherapy (AUC~0.9). Interval analysis may thus, in perspective, increase the diagnostic yield of interictal scalp EEG. Our findings point to the possible existence of alpha rhythm abnormalities in patients with epilepsy.
Deep Neural Architectures for Mapping Scalp to Intracranial EEG.
Antoniades, Andreas; Spyrou, Loukianos; Martin-Lopez, David; Valentin, Antonio; Alarcon, Gonzalo; Sanei, Saeid; Took, Clive Cheong
Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.
Short analysis of the increase of the EEG apportionment 2013; Kurzanalyse des Anstiegs der EEG-Umlage 2013
Energy Technology Data Exchange (ETDEWEB)
Loreck, Charlotte; Matthes, Felix C.; Hermann, Hauke; Jung, Frederieke; Emele, Lukas
At 15th October, 2012 the transmission system operators had published the EEG apportionment (EEG - Energy Economy Law). For the year 2013. This apportionment amounts 5,277 ct/kWh for non-privileged consumers in comparison to 3,59 ct/kWh for the year 2012. The ongoing enhancement of the renewable energies increases the EEG apportionment by an amount of 0.74 ct/kWh. With 0.26 ct/kWh the photovoltaics has the largest proportion in comparison to photovoltaics. The power generation from biomass as well as from wind energy at onshore sites contribute with 0.21 ct/kWh to the EEG apportionment. The greatest item of 0.48 ct/kWh is the debit balancing of the EEG account. The liquidity reserve for the year 2013 will be enhanced to 10% of the budget deficit. The expansion of the privileged status of the power consumption increases the EEG apportionment by 0.12 ct/kWh.
Use Case Analysis: The Ambulatory EEG in Navy Medicine for Traumatic Brain Injuries
science of binaural beats . Retrieved from http://binauralbrains.com/the-science-of- binaural - beats / Biosignal. (2016). MicroEEG. Retrieved from http...Cap. Source: Binaural Brains (n.d.). ....................................4 Figure 3. EEG Machine. Source: Refine Medical Technology (n.d...EEG. Figures 2, 3, and 4 display images of a standard EEG cap, EEG machine, and an EEG recording. Figure 2. Standard EEG Cap. Source: Binaural Brains
Nonlinear principal component analysis and its applications
Mori, Yuichi; Makino, Naomichi
This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed...
Early EEG for outcome prediction of postanoxic coma : Prospective cohort study with cost-minimization analysis
Sondag, Lotte; Ruijter, Barry J.; Tjepkema-Cloostermans, Marleen C.; Beishuizen, Albertus; Bosch, Frank H.; van Til, Janine A.; van Putten, Michel J.A.M.; Hofmeijer, Jeannette
Background: We recently showed that electroencephalography (EEG) patterns within the first 24 hours robustly contribute to multimodal prediction of poor or good neurological outcome of comatose patients after cardiac arrest. Here, we confirm these results and present a cost-minimization analysis.
Patient Specific Characteristic of Brain Dynamic in Interpretation of Long Term EEG Analysis
Czech Academy of Sciences Publication Activity Database
Komárek, V.; Paluš, Milan; Hrnčíř, Z.
Roč. 45, Suppl. 3 (2004), s. 51 ISSN 0013-9580. [European Congress on Epileptology /6./. 30.05.2004-03.06.2004, Vienna] R&D Projects: GA MŠk ME 701 Institutional research plan: CEZ:AV0Z1030915 Keywords : brain dynamic * long term EEG analysis Subject RIV: FH - Neurology
FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data
Oostenveld, R.; Fries, P.; Maris, E.G.G.; Schoffelen, J.M.
This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow
Sex Differences in the Sleep EEG of Young Adults : Visual Scoring and Spectral Analysis
Dijk, Derk Jan; Beersma, Domien G.M.; Bloem, Gerda M.
Baseline sleep of 13 men (mean age of 23.5 years) and 15 women (21.9 years) was analyzed. Visual scoring of the electroencephalograms (EEGs) revealed no significant differences between the sexes in the amounts of slow-wave sleep and rapid-eye-movement (REM) sleep. Spectral analysis, however,
Analysis of absence seizure generation using EEG spatial-temporal regularity measures.
Mammone, Nadia; Labate, Domenico; Lay-Ekuakille, Aime; Morabito, Francesco C
Epileptic seizures are thought to be generated and to evolve through an underlying anomaly of synchronization in the activity of groups of neuronal populations. The related dynamic scenario of state transitions is revealed by detecting changes in the dynamical properties of Electroencephalography (EEG) signals. The recruitment procedure ending with the crisis can be explored through a spatial-temporal plot from which to extract suitable descriptors that are able to monitor and quantify the evolving synchronization level from the EEG tracings. In this paper, a spatial-temporal analysis of EEG recordings based on the concept of permutation entropy (PE) is proposed. The performance of PE are tested on a database of 24 patients affected by absence (generalized) seizures. The results achieved are compared to the dynamical behavior of the EEG of 40 healthy subjects. Being PE a feature which is dependent on two parameters, an extensive study of the sensitivity of the performance of PE with respect to the parameters' setting was carried out on scalp EEG. Once the optimal PE configuration was determined, its ability to detect the different brain states was evaluated. According to the results here presented, it seems that the widely accepted model of "jump" transition to absence seizure should be in some cases coupled (or substituted) by a gradual transition model characteristic of self-organizing networks. Indeed, it appears that the transition to the epileptic status is heralded before the preictal state, ever since the interictal stages. As a matter of fact, within the limits of the analyzed database, the frontal-temporal scalp areas appear constantly associated to PE levels higher compared to the remaining electrodes, whereas the parieto-occipital areas appear associated to lower PE values. The EEG of healthy subjects neither shows any similar dynamic behavior nor exhibits any recurrent portrait in PE topography.
Spatiotemporal source analysis in scalp EEG vs. intracerebral EEG and SPECT: a case study in a 2-year-old child.
Aarabi, A; Grebe, R; Berquin, P; Bourel Ponchel, E; Jalin, C; Fohlen, M; Bulteau, C; Delalande, O; Gondry, C; Héberlé, C; Moullart, V; Wallois, F
This case study aims to demonstrate that spatiotemporal spike discrimination and source analysis are effective to monitor the development of sources of epileptic activity in time and space. Therefore, they can provide clinically useful information allowing a better understanding of the pathophysiology of individual seizures with time- and space-resolved characteristics of successive epileptic states, including interictal, preictal, postictal, and ictal states. High spatial resolution scalp EEGs (HR-EEG) were acquired from a 2-year-old girl with refractory central epilepsy and single-focus seizures as confirmed by intracerebral EEG recordings and ictal single-photon emission computed tomography (SPECT). Evaluation of HR-EEG consists of the following three global steps: (1) creation of the initial head model, (2) automatic spike and seizure detection, and finally (3) source localization. During the source localization phase, epileptic states are determined to allow state-based spike detection and localization of underlying sources for each spike. In a final cluster analysis, localization results are integrated to determine the possible sources of epileptic activity. The results were compared with the cerebral locations identified by intracerebral EEG recordings and SPECT. The results obtained with this approach were concordant with those of MRI, SPECT and distribution of intracerebral potentials. Dipole cluster centres found for spikes in interictal, preictal, ictal and postictal states were situated an average of 6.3mm from the intracerebral contacts with the highest voltage. Both amplitude and shape of spikes change between states. Dispersion of the dipoles was higher in the preictal state than in the postictal state. Two clusters of spikes were identified. The centres of these clusters changed position periodically during the various epileptic states. High-resolution surface EEG evaluated by an advanced algorithmic approach can be used to investigate the
Mutual information measures applied to EEG signals for sleepiness characterization
Melia, Umberto Sergio Pio; Guaita, Marc; Vallverdú Ferrer, Montserrat; Embid, Cristina; Vilaseca, I; Salamero, Manuel; Santamaria, Joan
Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep lat...
A method for nonlinear exponential regression analysis
Junkin, B. G.
A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.
Dvořáček, Michael
Vznik EEG aktivity v mozku, rozdělení EEG vln podle frekvence, způsob měření EEG, přístroje pro měření EEG. Dále popis biofeedback metody, její možnosti a návrh biofeedback her. Popis zpracování naměřených EEG signálů. EEG generation, brain rhythms, methods of recording EEG, EEG recorder. Description of biofeedback, potentialities of biofeedback, proposal of biofeedback games. Description of processing measured EEG signals. B
Simultaneous EEG/fMRI analysis of the resonance phenomena in steady-state visual evoked responses.
Bayram, Ali; Bayraktaroglu, Zubeyir; Karahan, Esin; Erdogan, Basri; Bilgic, Basar; Ozker, Muge; Kasikci, Itir; Duru, Adil D; Ademoglu, Ahmet; Oztürk, Cengizhan; Arikan, Kemal; Tarhan, Nevzat; Demiralp, Tamer
The stability of the steady-state visual evoked potentials (SSVEPs) across trials and subjects makes them a suitable tool for the investigation of the visual system. The reproducible pattern of the frequency characteristics of SSVEPs shows a global amplitude maximum around 10 Hz and additional local maxima around 20 and 40 Hz, which have been argued to represent resonant behavior of damped neuronal oscillators. Simultaneous electroencephalogram/functional magnetic resonance imaging (EEG/fMRI) measurement allows testing of the resonance hypothesis about the frequency-selective increases in SSVEP amplitudes in human subjects, because the total synaptic activity that is represented in the fMRI-Blood Oxygen Level Dependent (fMRI-BOLD) response would not increase but get synchronized at the resonance frequency. For this purpose, 40 healthy volunteers were visually stimulated with flickering light at systematically varying frequencies between 6 and 46 Hz, and the correlations between SSVEP amplitudes and the BOLD responses were computed. The SSVEP frequency characteristics of all subjects showed 3 frequency ranges with an amplitude maximum in each of them, which roughly correspond to alpha, beta and gamma bands of the EEG. The correlation maps between BOLD responses and SSVEP amplitude changes across the different stimulation frequencies within each frequency band showed no significant correlation in the alpha range, while significant correlations were obtained in the primary visual area for the beta and gamma bands. This non-linear relationship between the surface recorded SSVEP amplitudes and the BOLD responses of the visual cortex at stimulation frequencies around the alpha band supports the view that a resonance at the tuning frequency of the thalamo-cortical alpha oscillator in the visual system is responsible for the global amplitude maximum of the SSVEP around 10 Hz. Information gained from the SSVEP/fMRI analyses in the present study might be extrapolated to the
Human brain networks in physiological aging: a graph theoretical analysis of cortical connectivity from EEG data.
Vecchio, Fabrizio; Miraglia, Francesca; Bramanti, Placido; Rossini, Paolo Maria
Modern analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity. To test the hypothesis that aging processes modulate the brain connectivity network, EEG recording was conducted on 113 healthy volunteers. They were divided into three groups in accordance with their ages: 36 Young (15-45 years), 46 Adult (50-70 years), and 31 Elderly (>70 years). To evaluate the stability of the investigated parameters, a subgroup of 10 subjects underwent a second EEG recording two weeks later. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by eLORETA on cortical sources. EEG frequency bands of interest were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). The spectral connectivity analysis of cortical sources showed that the normalized Characteristic Path Length (λ) presented the pattern Young > Adult>Elderly in the higher alpha band. Elderly also showed a greater increase in delta and theta bands than Young. The correlation between age and λ showed that higher ages corresponded to higher λ in delta and theta and lower in the alpha2 band; this pattern reflects the age-related modulation of higher (alpha) and decreased (delta) connectivity. The Normalized Clustering coefficient (γ) and small-world network modeling (σ) showed non-significant age-modulation. Evidence from the present study suggests that graph theory can aid in the analysis of connectivity patterns estimated from EEG and can facilitate the study of the physiological and pathological brain aging features of functional connectivity networks.
Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features.
Lin, Lung-Chang; Chen, Sharon Chia-Ju; Chiang, Ching-Tai; Wu, Hui-Chuan; Yang, Rei-Cheng; Ouyang, Chen-Sen
The life quality of patients with refractory epilepsy is extremely affected by abrupt and unpredictable seizures. A reliable method for predicting seizures is important in the management of refractory epilepsy. A critical factor in seizure prediction involves the classification of the preictal and interictal stages. This study aimed to develop an efficient, automatic, quantitative, and individualized approach for preictal/interictal stage identification. Five epileptic children, who had experienced at least 2 episodes of seizures during a 24-hour video EEG recording, were included. Artifact-free preictal and interictal EEG epochs were acquired, respectively, and characterized with 216 global feature descriptors. The best subset of 5 discriminative descriptors was identified. The best subsets showed differences among the patients. Statistical analysis revealed most of the 5 descriptors in each subset were significantly different between the preictal and interictal stages for each patient. The proposed approach yielded weighted averages of 97.50% correctness, 96.92% sensitivity, 97.78% specificity, and 95.45% precision on classifying test epochs. Although the case number was limited, this study successfully integrated a new EEG analytical method to classify preictal and interictal EEG segments and might be used further in predicting the occurrence of seizures.
Quantitative EEG analysis in minimally conscious state patients during postural changes.
Greco, A; Carboncini, M C; Virgillito, A; Lanata, A; Valenza, G; Scilingo, E P
Mobilization and postural changes of patients with cognitive impairment are standard clinical practices useful for both psychic and physical rehabilitation process. During this process, several physiological signals, such as Electroen-cephalogram (EEG), Electrocardiogram (ECG), Photopletysmography (PPG), Respiration activity (RESP), Electrodermal activity (EDA), are monitored and processed. In this paper we investigated how quantitative EEG (qEEG) changes with postural modifications in minimally conscious state patients. This study is quite novel and no similar experimental data can be found in the current literature, therefore, although results are very encouraging, a quantitative analysis of the cortical area activated in such postural changes still needs to be deeply investigated. More specifically, this paper shows EEG power spectra and brain symmetry index modifications during a verticalization procedure, from 0 to 60 degrees, of three patients in Minimally Consciousness State (MCS) with focused region of impairment. Experimental results show a significant increase of the power in β band (12 - 30 Hz), commonly associated to human alertness process, thus suggesting that mobilization and postural changes can have beneficial effects in MCS patients.
[Compressive-spectral analysis of EEG in patients with panic attacks in the context of different psychiatric diseases].
Tuter, N V; Gnezditskiĭ, V V
Panic disorders (PD) which develop in the context of different psychiatric diseases (neurotic, personality disorder and schizotypal disorders) have their own clinical and neurophysiological features. The results of compressive-spectral analysis of EEG (CSA EEG) in patients with panic attack were different depending on the specifics of initial psychiatric status. EEG parameters in patients differed from those in controls. The common feature for all PD patients was the lower spectral density of theta-, alpha- and beta-bands as well as total spectral density without any alterations of region distribution. The decrease of electrical activity of activation systems was found in the groups with neurotic and schizotypal disorders and that of inhibition systems - in the group with schizotypal disorders. The EEG results did not suggest any depression of activation systems in patients with specific personality disorders. The data obtained with CSA EEG mirror the integrative brain activity which determinad of the appearance of PA as well as of nosology of psychiatre disease.
Comparison of Amplitude-Integrated EEG and Conventional EEG in a Cohort of Premature Infants.
Meledin, Irina; Abu Tailakh, Muhammad; Gilat, Shlomo; Yogev, Hagai; Golan, Agneta; Novack, Victor; Shany, Eilon
To compare amplitude-integrated EEG (aEEG) and conventional EEG (EEG) activity in premature neonates. Biweekly aEEG and EEG were simultaneously recorded in a cohort of infants born less than 34 weeks gestation. aEEG recordings were visually assessed for lower and upper border amplitude and bandwidth. EEG recordings were compressed for visual evaluation of continuity and assessed using a signal processing software for interburst intervals (IBI) and frequencies' amplitude. Ten-minute segments of aEEG and EEG indices were compared using regression analysis. A total of 189 recordings from 67 infants were made, from which 1697 aEEG/EEG pairs of 10-minute segments were assessed. Good concordance was found for visual assessment of continuity between the 2 methods. EEG IBI, alpha and theta frequencies' amplitudes were negatively correlated to the aEEG lower border while conceptional age (CA) was positively correlated to aEEG lower border ( P continuity and amplitude.
A new similarity index for nonlinear signal analysis based on local extrema patterns
Niknazar, Hamid; Motie Nasrabadi, Ali; Shamsollahi, Mohammad Bagher
Common similarity measures of time domain signals such as cross-correlation and Symbolic Aggregate approximation (SAX) are not appropriate for nonlinear signal analysis. This is because of the high sensitivity of nonlinear systems to initial points. Therefore, a similarity measure for nonlinear signal analysis must be invariant to initial points and quantify the similarity by considering the main dynamics of signals. The statistical behavior of local extrema (SBLE) method was previously proposed to address this problem. The SBLE similarity index uses quantized amplitudes of local extrema to quantify the dynamical similarity of signals by considering patterns of sequential local extrema. By adding time information of local extrema as well as fuzzifying quantized values, this work proposes a new similarity index for nonlinear and long-term signal analysis, which extends the SBLE method. These new features provide more information about signals and reduce noise sensitivity by fuzzifying them. A number of practical tests were performed to demonstrate the ability of the method in nonlinear signal clustering and classification on synthetic data. In addition, epileptic seizure detection based on electroencephalography (EEG) signal processing was done by the proposed similarity to feature the potentials of the method as a real-world application tool.
Nonparametric inference in nonlinear principal components analysis : exploration and beyond
Linting, Mariëlle
In the social and behavioral sciences, data sets often do not meet the assumptions of traditional analysis methods. Therefore, nonlinear alternatives to traditional methods have been developed. This thesis starts with a didactic discussion of nonlinear principal components analysis (NLPCA),
Analysis of brain activity and response to colour stimuli during learning tasks: an EEG study
Folgieri, Raffaella; Lucchiari, Claudio; Marini, Daniele
The research project intends to demonstrate how EEG detection through BCI device can improve the analysis and the interpretation of colours-driven cognitive processes through the combined approach of cognitive science and information technology methods. To this end, firstly it was decided to design an experiment based on comparing the results of the traditional (qualitative and quantitative) cognitive analysis approach with the EEG signal analysis of the evoked potentials. In our case, the sensorial stimulus is represented by the colours, while the cognitive task consists in remembering the words appearing on the screen, with different combination of foreground (words) and background colours. In this work we analysed data collected from a sample of students involved in a learning process during which they received visual stimuli based on colour variation. The stimuli concerned both the background of the text to learn and the colour of the characters. The experiment indicated some interesting results concerning the use of primary (RGB) and complementary (CMY) colours.
Probabilistic analysis of a materially nonlinear structure
Millwater, H. R.; Wu, Y.-T.; Fossum, A. F.
A probabilistic finite element program is used to perform probabilistic analysis of a materially nonlinear structure. The program used in this study is NESSUS (Numerical Evaluation of Stochastic Structure Under Stress), under development at Southwest Research Institute. The cumulative distribution function (CDF) of the radial stress of a thick-walled cylinder under internal pressure is computed and compared with the analytical solution. In addition, sensitivity factors showing the relative importance of the input random variables are calculated. Significant plasticity is present in this problem and has a pronounced effect on the probabilistic results. The random input variables are the material yield stress and internal pressure with Weibull and normal distributions, respectively. The results verify the ability of NESSUS to compute the CDF and sensitivity factors of a materially nonlinear structure. In addition, the ability of the Advanced Mean Value (AMV) procedure to assess the probabilistic behavior of structures which exhibit a highly nonlinear response is shown. Thus, the AMV procedure can be applied with confidence to other structures which exhibit nonlinear behavior.
Time-varying bispectral analysis of visually evoked multi-channel EEG
Chandran, Vinod
Theoretical foundations of higher order spectral analysis are revisited to examine the use of time-varying bicoherence on non-stationary signals using a classical short-time Fourier approach. A methodology is developed to apply this to evoked EEG responses where a stimulus-locked time reference is available. Short-time windowed ensembles of the response at the same offset from the reference are considered as ergodic cyclostationary processes within a non-stationary random process. Bicoherence can be estimated reliably with known levels at which it is significantly different from zero and can be tracked as a function of offset from the stimulus. When this methodology is applied to multi-channel EEG, it is possible to obtain information about phase synchronization at different regions of the brain as the neural response develops. The methodology is applied to analyze evoked EEG response to flash visual stimulii to the left and right eye separately. The EEG electrode array is segmented based on bicoherence evolution with time using the mean absolute difference as a measure of dissimilarity. Segment maps confirm the importance of the occipital region in visual processing and demonstrate a link between the frontal and occipital regions during the response. Maps are constructed using bicoherence at bifrequencies that include the alpha band frequency of 8Hz as well as 4 and 20Hz. Differences are observed between responses from the left eye and the right eye, and also between subjects. The methodology shows potential as a neurological functional imaging technique that can be further developed for diagnosis and monitoring using scalp EEG which is less invasive and less expensive than magnetic resonance imaging.
Perturbation analysis of nonlinear matrix population models
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Hal Caswell
Full Text Available Perturbation analysis examines the response of a model to changes in its parameters. It is commonly applied to population growth rates calculated from linear models, but there has been no general approach to the analysis of nonlinear models. Nonlinearities in demographic models may arise due to density-dependence, frequency-dependence (in 2-sex models, feedback through the environment or the economy, and recruitment subsidy due to immigration, or from the scaling inherent in calculations of proportional population structure. This paper uses matrix calculus to derive the sensitivity and elasticity of equilibria, cycles, ratios (e.g. dependency ratios, age averages and variances, temporal averages and variances, life expectancies, and population growth rates, for both age-classified and stage-classified models. Examples are presented, applying the results to both human and non-human populations.
Nonlinear analysis approximation theory, optimization and applications
Many of our daily-life problems can be written in the form of an optimization problem. Therefore, solution methods are needed to solve such problems. Due to the complexity of the problems, it is not always easy to find the exact solution. However, approximate solutions can be found. The theory of the best approximation is applicable in a variety of problems arising in nonlinear functional analysis and optimization. This book highlights interesting aspects of nonlinear analysis and optimization together with many applications in the areas of physical and social sciences including engineering. It is immensely helpful for young graduates and researchers who are pursuing research in this field, as it provides abundant research resources for researchers and post-doctoral fellows. This will be a valuable addition to the library of anyone who works in the field of applied mathematics, economics and engineering.
A distributed microcomputer-controlled system for data acquisition and power spectral analysis of EEG.
Vo, T D; Dwyer, G; Szeto, H H
A relatively powerful and inexpensive microcomputer-based system for the spectral analysis of the EEG is presented. High resolution and speed is achieved with the use of recently available large-scale integrated circuit technology with enhanced functionality (INTEL Math co-processors 8087) which can perform transcendental functions rapidly. The versatility of the system is achieved with a hardware organization that has distributed data acquisition capability performed by the use of a microprocessor-based analog to digital converter with large resident memory (Cyborg ISAAC-2000). Compiled BASIC programs and assembly language subroutines perform on-line or off-line the fast Fourier transform and spectral analysis of the EEG which is stored as soft as well as hard copy. Some results obtained from test application of the entire system in animal studies are presented.
Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal.
Namazi, Hamidreza; Khosrowabadi, Reza; Hussaini, Jamal; Habibi, Shaghayegh; Farid, Ali Akhavan; Kulish, Vladimir V
One of the major challenges in brain research is to relate the structural features of the auditory stimulus to structural features of Electroencephalogram (EEG) signal. Memory content is an important feature of EEG signal and accordingly the brain. On the other hand, the memory content can also be considered in case of stimulus. Beside all works done on analysis of the effect of stimuli on human EEG and brain memory, no work discussed about the stimulus memory and also the relationship that may exist between the memory content of stimulus and the memory content of EEG signal. For this purpose we consider the Hurst exponent as the measure of memory. This study reveals the plasticity of human EEG signals in relation to the auditory stimuli. For the first time we demonstrated that the memory content of an EEG signal shifts towards the memory content of the auditory stimulus used. The results of this analysis showed that an auditory stimulus with higher memory content causes a larger increment in the memory content of an EEG signal. For the verification of this result, we benefit from approximate entropy as indicator of time series randomness. The capability, observed in this research, can be further investigated in relation to human memory.
Analysis of EEG activity during sleep - brain hemisphere symmetry of two classes of sleep spindles
Smolen, Magdalena M.
This paper presents automatic analysis of some selected human electroencephalographic patterns during deep sleep using the Matching Pursuit (MP) algorithm. The periodicity of deep sleep EEG patterns was observed by calculating autocorrelation functions of their percentage contributions. The study confirmed the increasing trend of amplitude-weighted average frequency of sleep spindles from frontal to posterior derivations. The dominant frequencies from the left and the right brain hemisphere were strongly correlated.
Stability analysis of nonlinear systems with slope restricted nonlinearities.
Liu, Xian; Du, Jiajia; Gao, Qing
The problem of absolute stability of Lur'e systems with sector and slope restricted nonlinearities is revisited. Novel time-domain and frequency-domain criteria are established by using the Lyapunov method and the well-known Kalman-Yakubovich-Popov (KYP) lemma. The criteria strengthen some existing results. Simulations are given to illustrate the efficiency of the results.
Stability Analysis of Nonlinear Systems with Slope Restricted Nonlinearities
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Xian Liu
Full Text Available The problem of absolute stability of Lur'e systems with sector and slope restricted nonlinearities is revisited. Novel time-domain and frequency-domain criteria are established by using the Lyapunov method and the well-known Kalman-Yakubovich-Popov (KYP lemma. The criteria strengthen some existing results. Simulations are given to illustrate the efficiency of the results.
Nonlinear analysis of river flow time sequences
Porporato, Amilcare; Ridolfi, Luca
Within the field of chaos theory several methods for the analysis of complex dynamical systems have recently been proposed. In light of these ideas we study the dynamics which control the behavior over time of river flow, investigating the existence of a low-dimension deterministic component. The present article follows the research undertaken in the work of Porporato and Ridolfi [1996a] in which some clues as to the existence of chaos were collected. Particular emphasis is given here to the problem of noise and to nonlinear prediction. With regard to the latter, the benefits obtainable by means of the interpolation of the available time series are reported and the remarkable predictive results attained with this nonlinear method are shown.
EEG spectral analysis of attention in ADHD: implications for neurofeedback training?
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Hartmut eHeinrich
Full Text Available Objective: In children with attention-deficit/hyperactivity disorder (ADHD, an increased theta/beta ratio in the resting EEG typically serves as a rationale to conduct theta/beta neurofeedback training. However, this finding is increasingly challenged. As neurofeedback may rather target an active than a passive state, we studied the EEG in a condition that requires attention.Methods: In children with ADHD of the DSM-IV combined type (ADHD-C; N=15 and of the predominantly inattentive type (ADHD-I; N=9 and in typically developing children (N=19, EEG spectral analysis was conducted for segments during the attention network test without processing of stimuli and overt behavior. Frontal (F3, Fz, F4, central (C3, Cz, C4 and parietal (P3, Pz, P4 electrodes were included in the statistical analysis. To investigate if EEG spectral parameters are related to performance measures, correlation coefficients were calculated.Results: Particularly in the ADHD-C group, higher theta and alpha activity was found with the most prominent effect in the upper-theta/lower-alpha (5.5-10.5 Hz range. In the ADHD-I group, a significantly higher theta/beta ratio was observed at single electrodes (F3, Fz and a tendency for a higher theta/beta ratio when considering all electrodes (large effect size. Higher 5.5-10.5 Hz activity was associated with higher reaction time variability with the effect most prominent in the ADHD-C group. A higher theta/beta ratio was associated with higher reaction times, particularly in the ADHD-I group.Conclusions: 1. In an attention demanding period, children with ADHD are characterized by an underactivated state in the EEG with subtype-specific differences. 2. The functional relevance of related EEG parameters is indicated by associations with performance (reaction time measures. 3. Findings provide a rationale for applying NF protocols targeting theta (and alpha activity and the theta/beta ratio in subgroups of children with ADHD.
Combined analysis of cortical (EEG) and nerve stump signals improves robotic hand control.
Tombini, Mario; Rigosa, Jacopo; Zappasodi, Filippo; Porcaro, Camillo; Citi, Luca; Carpaneto, Jacopo; Rossini, Paolo Maria; Micera, Silvestro
Interfacing an amputee's upper-extremity stump nerves to control a robotic hand requires training of the individual and algorithms to process interactions between cortical and peripheral signals. To evaluate for the first time whether EEG-driven analysis of peripheral neural signals as an amputee practices could improve the classification of motor commands. Four thin-film longitudinal intrafascicular electrodes (tf-LIFEs-4) were implanted in the median and ulnar nerves of the stump in the distal upper arm for 4 weeks. Artificial intelligence classifiers were implemented to analyze LIFE signals recorded while the participant tried to perform 3 different hand and finger movements as pictures representing these tasks were randomly presented on a screen. In the final week, the participant was trained to perform the same movements with a robotic hand prosthesis through modulation of tf-LIFE-4 signals. To improve the classification performance, an event-related desynchronization/synchronization (ERD/ERS) procedure was applied to EEG data to identify the exact timing of each motor command. Real-time control of neural (motor) output was achieved by the participant. By focusing electroneurographic (ENG) signal analysis in an EEG-driven time window, movement classification performance improved. After training, the participant regained normal modulation of background rhythms for movement preparation (α/β band desynchronization) in the sensorimotor area contralateral to the missing limb. Moreover, coherence analysis found a restored α band synchronization of Rolandic area with frontal and parietal ipsilateral regions, similar to that observed in the opposite hemisphere for movement of the intact hand. Of note, phantom limb pain (PLP) resolved for several months. Combining information from both cortical (EEG) and stump nerve (ENG) signals improved the classification performance compared with tf-LIFE signals processing alone; training led to cortical reorganization and
Complexity analysis of EEG in patients with schizophrenia using fractal dimension
International Nuclear Information System (INIS)
Raghavendra, B S; Dutt, D Narayana; Halahalli, Harsha N; John, John P
We computed Higuchi's fractal dimension (FD) of resting, eyes closed EEG recorded from 30 scalp locations in 18 male neuroleptic-naïve, recent-onset schizophrenia (NRS) subjects and 15 male healthy control (HC) subjects, who were group-matched for age. Schizophrenia patients showed a diffuse reduction of FD except in the bilateral temporal and occipital regions, with the reduction being most prominent bifrontally. The positive symptom (PS) schizophrenia subjects showed FD values similar to or even higher than HC in the bilateral temporo-occipital regions, along with a co-existent bifrontal FD reduction as noted in the overall sample of NRS. In contrast, this increase in FD values in the bilateral temporo-occipital region was absent in the negative symptom (NS) subgroup. The regional differences in complexity suggested by these findings may reflect the aberrant brain dynamics underlying the pathophysiology of schizophrenia and its symptom dimensions. Higuchi's method of measuring FD directly in the time domain provides an alternative for the more computationally intensive nonlinear methods of estimating EEG complexity
Analysis of nonlinear systems using ARMA [autoregressive moving average] models
International Nuclear Information System (INIS)
Hunter, N.F. Jr.
While many vibration systems exhibit primarily linear behavior, a significant percentage of the systems encountered in vibration and model testing are mildly to severely nonlinear. Analysis methods for such nonlinear systems are not yet well developed and the response of such systems is not accurately predicted by linear models. Nonlinear ARMA (autoregressive moving average) models are one method for the analysis and response prediction of nonlinear vibratory systems. In this paper we review the background of linear and nonlinear ARMA models, and illustrate the application of these models to nonlinear vibration systems. We conclude by summarizing the advantages and disadvantages of ARMA models and emphasizing prospects for future development. 14 refs., 11 figs
Parametric and Nonparametric EEG Analysis for the Evaluation of EEG Activity in Young Children with Controlled Epilepsy
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Vangelis Sakkalis
Full Text Available There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform and a parametric, signal modeling technique (ARMA are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.
A Study on Analysis of EEG Caused by Grating Stimulation Imaging
Urakawa, Hiroshi; Nishimura, Toshihiro; Tsubai, Masayoshi; Itoh, Kenji
Recently, many researchers have studied a visual perception. Focus is attended to studies of the visual perception phenomenon by using the grating stimulation images. The previous researches have suggested that a subset of retinal ganglion cells responds to motion in the receptive field center, but only if the wider surround moves with a different trajectory. We discuss the function of human retina, and measure and analysis EEG(electroencephalography) of a normal subject who looks on grating stimulation images. We confirmed the visual perception of human by EEG signal analysis. We also have obtained that a sinusoidal grating stimulation was given, asymmetry was observed the α wave element in EEG of the symmetric part in a left hemisphere and a right hemisphere of the brain. Therefore, it is presumed that projected image is even when the still picture is seen and the image projected onto retinas of right and left eyes is not even for the dynamic scene. It evaluated it by taking the envelope curve for the detected α wave, and using the average and standard deviation.
Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool
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Pinar Deniz Tosun
Full Text Available Specific patterns of brain activity during sleep and waking are recorded in the electroencephalogram (EEG. Time-frequency analysis methods have been widely used to analyse the EEG and identified characteristic oscillations for each vigilance state (VS, i.e., wakefulness, rapid-eye movement (REM and non-rapid-eye movement (NREM sleep. However, other aspects such as change of patterns associated with brain dynamics may not be captured unless a non-linear-based analysis method is used. In this pilot study, Permutation Lempel–Ziv complexity (PLZC, a novel symbolic dynamics analysis method, was used to characterise the changes in the EEG in sleep and wakefulness during baseline and recovery from sleep deprivation (SD. The results obtained with PLZC were contrasted with a related non-linear method, Lempel–Ziv complexity (LZC. Both measure the emergence of new patterns. However, LZC is dependent on the absolute amplitude of the EEG, while PLZC is only dependent on the relative amplitude due to symbolisation procedure and thus, more resistant to noise. We showed that PLZC discriminates activated brain states associated with wakefulness and REM sleep, which both displayed higher complexity, compared to NREM sleep. Additionally, significantly lower PLZC values were measured in NREM sleep during the recovery period following SD compared to baseline, suggesting a reduced emergence of new activity patterns in the EEG. These findings were validated using PLZC on surrogate data. By contrast, LZC was merely reflecting changes in the spectral composition of the EEG. Overall, this study implies that PLZC is a robust non-linear complexity measure, which is not dependent on amplitude variations in the signal, and which may be useful to further assess EEG alterations induced by environmental or pharmacological manipulations.
Nonlinear Dynamical Analysis for a Plain Bearing
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Ali Belhamra
Full Text Available This paper investigates the nonlinear dynamic behavior for a plain classic bearing (fluid bearing lubricated by a non-Newtonian fluid of a turbo machine rotating with high speed; this type of fluid contains additives viscosity (couple-stress fluid film. The solution of the nonlinear dynamic problem of this type of bearing is determined with a spatial discretisation of the modified Reynolds' equation written in dynamic mode by using the optimized short bearing theory and a temporal discretisation for equations of rotor motion by the help of Euler's explicit diagram. This study analyzes the dynamic behavior of a rotor supported by two couple-stress fluid film journal lubricant enhances the dynamic stability of the rotor-bearing system considerably compared to that obtained when using a traditional Newtonian lubricant. The analysis shows that the dynamic behavior of a shaft which turns with high velocities is strongly nonlinear even for poor eccentricities of unbalance; the presence of parameters of couple stress allows strongly attenuating the will synchrony (unbalance and asynchrony (whipping amplitudes of vibrations of the shaft which supports more severe conditions (large unbalances.
Spectral analysis of noisy nonlinear maps
International Nuclear Information System (INIS)
Hirshman, S.P.; Whitson, J.C.
A path integral equation formalism is developed to obtain the frequency spectrum of nonlinear mappings exhibiting chaotic behavior. The one-dimensional map, x/sub n+1/ = f(x/sub n/), where f is nonlinear and n is a discrete time variable, is analyzed in detail. This map is introduced as a paradigm of systems whose exact behavior is exceedingly complex, and therefore irretrievable, but which nevertheless possess smooth, well-behaved solutions in the presence of small sources of external noise. A Boltzmann integral equation is derived for the probability distribution function p(x,n). This equation is linear and is therefore amenable to spectral analysis. The nonlinear dynamics in f(x) appear as transition probability matrix elements, and the presence of noise appears simply as an overall multiplicative scattering amplitude. This formalism is used to investigate the band structure of the logistic equation and to analyze the effects of external noise on both the invariant measure and the frequency spectrum of x/sub n/ for several values of lambda epsilon [0,1
Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function.
Guo, Qian; Zhou, Tiantong; Li, Wenjie; Dong, Li; Wang, Suhong; Zou, Ling
Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG-fMRI fusion technology to reveal brain neural activity related with feedback stimuli. In this study, an EEG-informed fMRI analysis was applied to process simultaneous EEG-fMRI data. First, relative power-spectrum analysis and K-means clustering method were performed separately to extract EEG-fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG-fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback-related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta-band power was used as a regressor compared with the other two fusion results. The EEG-based fMRI analysis can more accurately depict the whole-brain activation map and analyze emotional decision problems.
Instantaneous 3D EEG Signal Analysis Based on Empirical Mode Decomposition and the Hilbert–Huang Transform Applied to Depth of Anaesthesia
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Mu-Tzu Shih
Full Text Available Depth of anaesthesia (DoA is an important measure for assessing the degree to which the central nervous system of a patient is depressed by a general anaesthetic agent, depending on the potency and concentration with which anaesthesia is administered during surgery. We can monitor the DoA by observing the patient's electroencephalography (EEG signals during the surgical procedure. Typically high frequency EEG signals indicates the patient is conscious, while low frequency signals mean the patient is in a general anaesthetic state. If the anaesthetist is able to observe the instantaneous frequency changes of the patient's EEG signals during surgery this can help to better regulate and monitor DoA, reducing surgical and post-operative risks. This paper describes an approach towards the development of a 3D real-time visualization application which can show the instantaneous frequency and instantaneous amplitude of EEG simultaneously by using empirical mode decomposition (EMD and the Hilbert–Huang transform (HHT. HHT uses the EMD method to decompose a signal into so-called intrinsic mode functions (IMFs. The Hilbert spectral analysis method is then used to obtain instantaneous frequency data. The HHT provides a new method of analyzing non-stationary and nonlinear time series data. We investigate this approach by analyzing EEG data collected from patients undergoing surgical procedures. The results show that the EEG differences between three distinct surgical stages computed by using sample entropy (SampEn are consistent with the expected differences between these stages based on the bispectral index (BIS, which has been shown to be quantifiable measure of the effect of anaesthetics on the central nervous system. Also, the proposed filtering approach is more effective compared to the standard filtering method in filtering out signal noise resulting in more consistent results than those provided by the BIS. The proposed approach is therefore
Scale-specific effects: A report on multiscale analysis of acupunctured EEG in entropy and power
Song, Zhenxi; Deng, Bin; Wei, Xile; Cai, Lihui; Yu, Haitao; Wang, Jiang; Wang, Ruofan; Chen, Yingyuan
Investigating acupuncture effects contributes to improving clinical application and understanding neuronal dynamics under external stimulation. In this report, we recorded electroencephalography (EEG) signals evoked by acupuncture at ST36 acupoint with three stimulus frequencies of 50, 100 and 200 times per minutes, and selected non-acupuncture EEGs as the control group. Multiscale analyses were introduced to investigate the possible acupuncture effects on complexity and power in multiscale level. Using multiscale weighted-permutation entropy, we found the significant effects on increased complexity degree in EEG signals induced by acupuncture. The comparison of three stimulation manipulations showed that 100 times/min generated most obvious effects, and affected most cortical regions. By estimating average power spectral density, we found decreased power induced by acupuncture. The joint distribution of entropy and power indicated an inverse correlation, and this relationship was weakened by acupuncture effects, especially under the manipulation of 100 times/min frequency. Above findings are more evident and stable in large scales than small scales, which suggests that multiscale analysis allows evaluating significant effects in specific scale and enables to probe the inherent characteristics underlying physiological signals.
Disentangling Tinnitus Distress and Tinnitus Presence by Means of EEG Power Analysis
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Martin Meyer
Full Text Available The present study investigated 24 individuals suffering from chronic tinnitus (TI and 24 nonaffected controls (CO. We recorded resting-state EEG and collected psychometric data to obtain information about how chronic tinnitus experience affects the cognitive and emotional state of TI. The study was meant to disentangle TI with high distress from those who suffer less from persistent tinnitus based on both neurophysiological and behavioral data. A principal component analysis of psychometric data uncovers two distinct independent dimensions characterizing the individual tinnitus experience. These independent states are distress and presence, the latter is described as the perceived intensity of sound experience that increases with tinnitus duration devoid of any considerable emotional burden. Neuroplastic changes correlate with the two independent components. TI with high distress display increased EEG activity in the oscillatory range around 25 Hz (upper β-band that agglomerates over frontal recording sites. TI with high presence show enhanced EEG signal strength in the δ-, α-, and lower γ-bands (30–40 Hz over bilateral temporal and left perisylvian electrodes. Based on these differential patterns we suggest that the two dimensions, namely, distress and presence, should be considered as independent dimensions of chronic subjective tinnitus.
The PREP Pipeline: Standardized preprocessing for large-scale EEG analysis
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Nima eBigdelys Shamlo
Full Text Available The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode/.
The PREP pipeline: standardized preprocessing for large-scale EEG analysis.
Bigdely-Shamlo, Nima; Mullen, Tim; Kothe, Christian; Su, Kyung-Min; Robbins, Kay A
The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.
P2-15: EEG Analysis on Story Change in TV Drama
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Chung-Yeon Lee
Full Text Available The human brain naturally recognizes a change of environment or atmosphere without great effort, and this is essential for interactive communication in social life and a specific reaction in an emergency situation. Most studies have investigated change detection of the brain with conditional experimental paradigms rather than the performance of everyday tasks. However, naturally occurring sensory stimuli are multimodal and dynamic. In an effort to study the relationship between users' induced physiological responses and changes of environment and atmosphere under more naturalistic and ecological conditions, we performed a basic experiment using audio-visual movies and electroencephalogram (EEG measurement. 8 healthy subjects were asked to watch a television sitcom without any responses, and their EEG signals were recorded simultaneously with 126 electrodes mounted in an elastic electrode cap. Time-frequency analysis of EEG revealed distinctive neural oscillations at the point of story change in the movie. This result could be used for applications in brain-computer interfaces, and provides a reference to cognitive impairment studies such as Attention Deficit Disorder (ADD or Attention-Deficit/Hyperactivity Disorder (ADHD.
Dysconnection topography in schizophrenia revealed with state-space analysis of EEG.
Jalili, Mahdi; Lavoie, Suzie; Deppen, Patricia; Meuli, Reto; Do, Kim Q; Cuénod, Michel; Hasler, Martin; De Feo, Oscar; Knyazeva, Maria G
The dysconnection hypothesis has been proposed to account for pathophysiological mechanisms underlying schizophrenia. Widespread structural changes suggesting abnormal connectivity in schizophrenia have been imaged. A functional counterpart of the structural maps would be the EEG synchronization maps. However, due to the limits of currently used bivariate methods, functional correlates of dysconnection are limited to the isolated measurements of synchronization between preselected pairs of EEG signals. To reveal a whole-head synchronization topography in schizophrenia, we applied a new method of multivariate synchronization analysis called S-estimator to the resting dense-array (128 channels) EEG obtained from 14 patients and 14 controls. This method determines synchronization from the embedding dimension in a state-space domain based on the theoretical consequence of the cooperative behavior of simultaneous time series-the shrinking of the state-space embedding dimension. The S-estimator imaging revealed a specific synchronization landscape in schizophrenia patients. Its main features included bilaterally increased synchronization over temporal brain regions and decreased synchronization over the postcentral/parietal region neighboring the midline. The synchronization topography was stable over the course of several months and correlated with the severity of schizophrenia symptoms. In particular, direct correlations linked positive, negative, and general psychopathological symptoms to the hyper-synchronized temporal clusters over both hemispheres. Along with these correlations, general psychopathological symptoms inversely correlated within the hypo-synchronized postcentral midline region. While being similar to the structural maps of cortical changes in schizophrenia, the S-maps go beyond the topography limits, demonstrating a novel aspect of the abnormalities of functional cooperation: namely, regionally reduced or enhanced connectivity. The new method of
Dysconnection topography in schizophrenia revealed with state-space analysis of EEG.
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Mahdi Jalili
Full Text Available The dysconnection hypothesis has been proposed to account for pathophysiological mechanisms underlying schizophrenia. Widespread structural changes suggesting abnormal connectivity in schizophrenia have been imaged. A functional counterpart of the structural maps would be the EEG synchronization maps. However, due to the limits of currently used bivariate methods, functional correlates of dysconnection are limited to the isolated measurements of synchronization between preselected pairs of EEG signals.To reveal a whole-head synchronization topography in schizophrenia, we applied a new method of multivariate synchronization analysis called S-estimator to the resting dense-array (128 channels EEG obtained from 14 patients and 14 controls. This method determines synchronization from the embedding dimension in a state-space domain based on the theoretical consequence of the cooperative behavior of simultaneous time series-the shrinking of the state-space embedding dimension. The S-estimator imaging revealed a specific synchronization landscape in schizophrenia patients. Its main features included bilaterally increased synchronization over temporal brain regions and decreased synchronization over the postcentral/parietal region neighboring the midline. The synchronization topography was stable over the course of several months and correlated with the severity of schizophrenia symptoms. In particular, direct correlations linked positive, negative, and general psychopathological symptoms to the hyper-synchronized temporal clusters over both hemispheres. Along with these correlations, general psychopathological symptoms inversely correlated within the hypo-synchronized postcentral midline region. While being similar to the structural maps of cortical changes in schizophrenia, the S-maps go beyond the topography limits, demonstrating a novel aspect of the abnormalities of functional cooperation: namely, regionally reduced or enhanced connectivity.The new
EEG-Based Analysis of the Emotional Effect of Music Therapy on Palliative Care Cancer Patients
Ramirez, Rafael; Planas, Josep; Escude, Nuria; Mercade, Jordi; Farriols, Cristina
Music is known to have the power to induce strong emotions. The present study assessed, based on Electroencephalography (EEG) data, the emotional response of terminally ill cancer patients to a music therapy intervention in a randomized controlled trial. A sample of 40 participants from the palliative care unit in the Hospital del Mar in Barcelona was randomly assigned to two groups of 20. The first group [experimental group (EG)] participated in a session of music therapy (MT), and the second group [control group (CG)] was provided with company. Based on our previous work on EEG-based emotion detection, instantaneous emotional indicators in the form of a coordinate in the arousal-valence plane were extracted from the participants' EEG data. The emotional indicators were analyzed in order to quantify (1) the overall emotional effect of MT on the patients compared to controls, and (2) the relative effect of the different MT techniques applied during each session. During each MT session, five conditions were considered: I (initial patient's state before MT starts), C1 (passive listening), C2 (active listening), R (relaxation), and F (final patient's state). EEG data analysis showed a significant increase in valence (p = 0.0004) and arousal (p = 0.003) between I and F in the EG. No significant changes were found in the CG. This results can be interpreted as a positive emotional effect of MT in advanced cancer patients. In addition, according to pre- and post-intervention questionnaire responses, participants in the EG also showed a significant decrease in tiredness, anxiety and breathing difficulties, as well as an increase in levels of well-being. No equivalent changes were observed in the CG. PMID:29551984
EEG-Based Analysis of the Emotional Effect of Music Therapy on Palliative Care Cancer Patients
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Rafael Ramirez
Full Text Available Music is known to have the power to induce strong emotions. The present study assessed, based on Electroencephalography (EEG data, the emotional response of terminally ill cancer patients to a music therapy intervention in a randomized controlled trial. A sample of 40 participants from the palliative care unit in the Hospital del Mar in Barcelona was randomly assigned to two groups of 20. The first group [experimental group (EG] participated in a session of music therapy (MT, and the second group [control group (CG] was provided with company. Based on our previous work on EEG-based emotion detection, instantaneous emotional indicators in the form of a coordinate in the arousal-valence plane were extracted from the participants' EEG data. The emotional indicators were analyzed in order to quantify (1 the overall emotional effect of MT on the patients compared to controls, and (2 the relative effect of the different MT techniques applied during each session. During each MT session, five conditions were considered: I (initial patient's state before MT starts, C1 (passive listening, C2 (active listening, R (relaxation, and F (final patient's state. EEG data analysis showed a significant increase in valence (p = 0.0004 and arousal (p = 0.003 between I and F in the EG. No significant changes were found in the CG. This results can be interpreted as a positive emotional effect of MT in advanced cancer patients. In addition, according to pre- and post-intervention questionnaire responses, participants in the EG also showed a significant decrease in tiredness, anxiety and breathing difficulties, as well as an increase in levels of well-being. No equivalent changes were observed in the CG.
On seeing the trees and the forest: single-signal and multisignal analysis of periictal intracranial EEG.
Schindler, Kaspar; Gast, Heidemarie; Goodfellow, Marc; Rummel, Christian
identified seizure-onset zone. When compared to the bit strings derived from other EEG signals, their M was relatively smaller. These findings are consistent with a strong but transient occurrence of information-poor, that is, redundant electrical brain activity on a smaller spatial scale, which is particularly pronounced in the seizure-onset zone. On a larger spatial scale, a progressively more collective state emerges, as revealed by increasing amounts of mutual information. Information theoretical analysis of bit patterns derived from EEG signals helps to characterize periictal brain activity on different spatial scales in a quantitative and efficient way and may provide clinically relevant results. Wiley Periodicals, Inc. © 2012 International League Against Epilepsy.
Nonlinear peculiar-velocity analysis and PCA
Energy Technology Data Exchange (ETDEWEB)
Dekel, A. [and others
We allow for nonlinear effects in the likelihood analysis of peculiar velocities, and obtain {approximately}35%-lower values for the cosmological density parameter and for the amplitude of mass-density fluctuations. The power spectrum in the linear regime is assumed to be of the flat {Lambda}CDM model (h = 0:65, n = 1) with only {Omega}{sub m} free. Since the likelihood is driven by the nonlinear regime, we break the power spectrum at k{sub b} {approximately} 0.2 (h{sup {minus}1} Mpc){sup {minus}1} and fit a two-parameter power-law at k > k{sub b} . This allows for an unbiased fit in the linear regime. Tests using improved mock catalogs demonstrate a reduced bias and a better fit. We find for the Mark III and SFI data {Omega}{sub m} = 0.35 {+-} 0.09 with {sigma}{sub 8}{Omega}P{sub m}{sup 0.6} = 0.55 {+-} 0.10 (90% errors). When allowing deviations from {Lambda}CDM, we find an indication for a wiggle in the power spectrum in the form of an excess near k {approximately} 0.05 and a deficiency at k {approximately} 0.1 (h{sup {minus}1} Mpc){sup {minus}1}--a cold flow which may be related to a feature indicated from redshift surveys and the second peak in the CMB anisotropy. A {chi}{sup 2} test applied to principal modes demonstrates that the nonlinear procedure improves the goodness of fit. The Principal Component Analysis (PCA) helps identifying spatial features of the data and fine-tuning the theoretical and error models. We address the potential for optimal data compression using PCA.
Dynamic nonlinear analysis of shells of revolution
International Nuclear Information System (INIS)
Von Riesemann, W.A.; Stricklin, J.A.; Haisler, W.E.
DYNAPLAS is a program for the transient response of ring stiffened shells of revolution subjected to either asymmetric initial velocities or to asymmetric pressure loadings. Both material and geometric nonlinearities may be considered. The present version, DYNAPLAS II, began with the programs SAMMSOR and DYNASOR. As is the case for the earlier programs, a driver program, SAMMSOR III, generates the stiffness and mass matrices for the harmonics under consideration. A highly refined meridionally curved axisymmetric thin shell of revolution element is used in conjunction with beam type ring stiffeners in the circumferential direction. The shell element uses a cubic displacement function and through static condensation a basic eight degree of freedom element is generated. The shell material may be isotropic or orthotropic. DYNAPLAS II uses the 'displacement' method of analysis in which the nonlinearities are treated as pseudo loads on the right-hand side of the equations of motion. The equations are written for each Fourier harmonic used in representing the asymmetric loading components, and although the left-hand side of the equations is uncoupled, the right-hand side is coupled by the nonlinear pseudo loads. The strain displacement equations of Novozhilov are used and the incremental theory of plasticity is used with the von Mises yield condition and associated flow rule. Either isotropic work hardening or the mechanical sublayer model may be used. Strain rate effects may be included. Either the explicit central difference method or the implcit Houbolt method are available. The program has found use in the analysis of containment vessels for light water reactors
Time history nonlinear earthquake response analysis considering materials and geometrical nonlinearity
International Nuclear Information System (INIS)
Kobayashi, T.; Yoshikawa, K.; Takaoka, E.; Nakazawa, M.; Shikama, Y.
A time history nonlinear earthquake response analysis method was proposed and applied to earthquake response prediction analysis for a Large Scale Seismic Test (LSST) Program in Hualien, Taiwan, in which a 1/4 scale model of a nuclear reactor containment structure was constructed on sandy gravel layer. In the analysis both of strain-dependent material nonlinearity, and geometrical nonlinearity by base mat uplift, were considered. The 'Lattice Model' for the soil-structure interaction model was employed. An earthquake record on soil surface at the site was used as control motion, and deconvoluted to the input motion of the analysis model at GL-52 m with 300 Gal of maximum acceleration. The following two analyses were considered: (A) time history nonlinear, (B) equivalent linear, and the advantage of time history nonlinear earthquake response analysis method is discussed
EEG spectral analysis and its clinical significance for patients with non-occupationalchronic mercury poisoning
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Bin-bin SUN
Full Text Available Objective To evaluate the features of EEG spectrum and its clinical significance for patients with non-occupational chronic mercury poisoning. Methods Eighteen patients with chronic mercury poisoning were collected continuously as poisoning group at Affiliated Hospital of Academy of Military Medical Sciences from March 2012 to September 2013. At the same time, 12 age- and sex-matched healthy people were selected as control group. All patients underwent video EEG, and EEGLAB in Matlab 2013 software was used to analyze their EEG data. Relevant spectrum data of the 2 groups were compared and analyzed. Results The frequency-energy curves of 12 normal subjects were similar to sine curve, with obvious energy peak at α band. The frequency-energy curves of 18 patients showed as follows: 5 cases had the peak at slow δ wave, and the energy curve decreased since δ band appeared, with α band peak disappearing. The curve of 10 cases had 2 peaks respectively at α and δ band, and δ peak was higher than α peak. The spectrum in other 3 cases was normal. The quantitative analysis of EEG revealed the proportion of δ band for the total energy. The proportion of δ band for total energy of the poisoning group in right middle temporal (P = 0.018 and left posterior temporal (P = 0.039 channel was significantly higher than that of the normal group, while the proportion of δ band in middle frontal (P = 0.003, right frontal (P = 0.016 and right anterior temporal (P = 0.024, left middle temporal (P = 0.036 and right posterior temporal (P = 0.031 was lower than that of the normal group. Conclusions EEG examination plays an important role in assessing the severity of brain injury for patients with non-occupational chronic mercury poisoning. Spectrum analysis is an intuitive and simple method, and can provide some help for clinical diagnosis and treatment. DOI: 10.3969/j.issn.1672-6731.2015.02.013
Using robust principal component analysis to alleviate day-to-day variability in EEG based emotion classification.
Ping-Keng Jao; Yuan-Pin Lin; Yi-Hsuan Yang; Tzyy-Ping Jung
An emerging challenge for emotion classification using electroencephalography (EEG) is how to effectively alleviate day-to-day variability in raw data. This study employed the robust principal component analysis (RPCA) to address the problem with a posed hypothesis that background or emotion-irrelevant EEG perturbations lead to certain variability across days and somehow submerge emotion-related EEG dynamics. The empirical results of this study evidently validated our hypothesis and demonstrated the RPCA's feasibility through the analysis of a five-day dataset of 12 subjects. The RPCA allowed tackling the sparse emotion-relevant EEG dynamics from the accompanied background perturbations across days. Sequentially, leveraging the RPCA-purified EEG trials from more days appeared to improve the emotion-classification performance steadily, which was not found in the case using the raw EEG features. Therefore, incorporating the RPCA with existing emotion-aware machine-learning frameworks on a longitudinal dataset of each individual may shed light on the development of a robust affective brain-computer interface (ABCI) that can alleviate ecological inter-day variability.
Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.
Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J
Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.
The Analysis of the Strength, Distribution and Direction for the EEG Phase Synchronization by Musical Stimulus
Ogawa, Yutaro; Ikeda, Akira; Kotani, Kiyoshi; Jimbo, Yasuhiko
In this study, we propose the EEG phase synchronization analysis including not only the average strength of the synchronization but also the distribution and directions under the conditions that evoked emotion by musical stimuli. The experiment is performed with the two different musical stimuli that evoke happiness or sadness for 150 seconds. It is found that the average strength of synchronization indicates no difference between the right side and the left side of the frontal lobe during the happy stimulus, the distribution and directions indicate significant differences. Therefore, proposed analysis is useful for detecting emotional condition because it provides information that cannot be obtained only by the average strength of synchronization.
Optimal Measurement Conditions for Spatiotemporal EEG/MEG Source Analysis.
Huizenga, Hilde M.; Heslenfeld, Dirk J.; Molenaar, Peter C. M.
Developed a method to determine the required number and position of sensors for human brain electromagnetic source analysis. Studied the method through a simulation study and an empirical study on visual evoked potentials in one adult male. Results indicate the method is fast and reliable and improves source precision. (SLD)
The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.
Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo
The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.
Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis.
Kaya, Yılmaz
This paper proposes a novel approach to detect epilepsy seizures by using Electroencephalography (EEG), which is one of the most common methods for the diagnosis of epilepsy, based on 1-Dimension Local Binary Pattern (1D-LBP) and grey relational analysis (GRA) methods. The main aim of this paper is to evaluate and validate a novel approach, which is a computer-based quantitative EEG analyzing method and based on grey systems, aimed to help decision-maker. In this study, 1D-LBP, which utilizes all data points, was employed for extracting features in raw EEG signals, Fisher score (FS) was employed to select the representative features, which can also be determined as hidden patterns. Additionally, GRA is performed to classify EEG signals through these Fisher scored features. The experimental results of the proposed approach, which was employed in a public dataset for validation, showed that it has a high accuracy in identifying epileptic EEG signals. For various combinations of epileptic EEG, such as A-E, B-E, C-E, D-E, and A-D clusters, 100, 96, 100, 99.00 and 100% were achieved, respectively. Also, this work presents an attempt to develop a new general-purpose hidden pattern determination scheme, which can be utilized for different categories of time-varying signals.
Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units
Caat, Michael ten; Maurits, Natasha M.; Roerdink, Jos B.T.M.
A typical data- driven visualization of electroencephalography ( EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter,
EEG feature selection method based on decision tree.
Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun
This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.
Nonlinear seismic analysis of continuous RC bridge
Directory of Open Access Journals (Sweden)
Čokić Miloš M.
Full Text Available Nonlinear static analysis, known as a pushover method (NSPA is oftenly used to study the behaviour of a bridge structure under the seismic action. It is shown that the Equivalent Linearization Method - ELM, recommended in FEMA 440, is appropriate for the response analysis of the bridge columns, with different geometric characteristics, quantity and distribution of steel reinforcement. The subject of analysis is a bridge structure with a carriageway plate - a continuous beam with three spans, with the 24 + 40 + 24 m range. Main girder is made of prestressed concrete and it has a box cross section of a constant height. It is important to study the behaviour, not only in the transverse, but also in the longitudinal direction of the bridge axis, when analysing the bridge columns exposed to horizontal seismic actions. The columns were designed according to EN1992, parts 1 and 2. Seismic action analysis is conducted according to EN 1998: 2004 standard. Response spectrum type 1, for the ground type B, was applied and the analysis also includes 20% of traffic load. The analysis includes the values of columns displacement and ductility. To describe the behaviour of elements under the earthquake action in both - longitudinal and transverse direction, pushover curves were formed.
Correlation between videogame mechanics and executive functions through EEG analysis.
Mondéjar, Tania; Hervás, Ramón; Johnson, Esperanza; Gutierrez, Carlos; Latorre, José Miguel
This paper addresses a different point of view of videogames, specifically serious games for health. This paper contributes to that area with a multidisciplinary perspective focus on neurosciences and computation. The experiment population has been pre-adolescents between the ages of 8 and 12 without any cognitive issues. The experiment consisted in users playing videogames as well as performing traditional psychological assessments; during these tasks the frontal brain activity was evaluated. The main goal was to analyse how the frontal lobe of the brain (executive function) works in terms of prominent cognitive skills during five types of game mechanics widely used in commercial videogames. The analysis was made by collecting brain signals during the two phases of the experiment, where the signals were analysed with an electroencephalogram neuroheadset. The validated hypotheses were whether videogames can develop executive functioning and if it was possible to identify which kind of cognitive skills are developed during each kind of typical videogame mechanic. The results contribute to the design of serious games for health purposes on a conceptual level, particularly in support of the diagnosis and treatment of cognitive-related pathologies. Copyright © 2016 Elsevier Inc. All rights reserved.
Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring
DEFF Research Database (Denmark)
Vilamala, Albert; Madsen, Kristoffer Hougaard; Hansen, Lars K.
to purse for an automatic stage scoring based on machine learning techniques have been carried out over the last years. In this work, we resort to multitaper spectral analysis to create visually interpretable images of sleep patterns from EEG signals as inputs to a deep convolutional network trained...... to solve visual recognition tasks. As a working example of transfer learning, a system able to accurately classify sleep stages in new unseen patients is presented. Evaluations in a widely-used publicly available dataset favourably compare to state-of-the-art results, while providing a framework for visual...
Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai
We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model...... for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may...
Functional community analysis of brain: a new approach for EEG-based investigation of the brain pathology.
Ahmadlou, Mehran; Adeli, Hojjat
Analysis of structure of the brain functional connectivity (SBFC) is a fundamental issue for understanding of the brain cognition as well as the pathology of brain disorders. Analysis of communities among sub-parts of a system is increasingly used for social, ecological, and other networks. This paper presents a new methodology for investigation of the SBFC and understanding of the brain based on graph theory and community pattern analysis of functional connectivity graph of the brain obtained from encephalograms (EEGs). The methodology consists of three main parts: fuzzy synchronization likelihood (FSL), community partitioning, and decisions based on partitions. As an example application, the methodology is applied to analysis of brain of patients with attention deficit/hyperactivity disorder (ADHD) and the problem of discrimination of ADHD EEGs from healthy (non-ADHD) EEGs. Copyright © 2011. Published by Elsevier Inc.
EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement
Shenoy Handiru, Vikram; Vinod, A. P.; Guan, Cuntai
Objective. In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.
Market and system integration of renewable energy sources. A legal analysis of the regulations for direct marketing in the EEG 2012; Markt- und Systemintegration der Erneuerbaren-Energien. Eine rechtliche Analyse der Regeln zur Direktvermarktung im EEG 2012
Energy Technology Data Exchange (ETDEWEB)
Lehnert, Wieland [Kanzlei Becker Buettner Held, Berlin (Germany)
The EEG 2012 presents optional funding instruments for direct marketing of EEG electricity, in particular a market bonus and a flexibility bonus. The author presents the new regulation for direct marketing in the EEG 2012, in consideration of the practice in energy law. Important aspects are a systematic integration of the new specifications in energy law and an analysis of their contents. Effects on the practice of energy management are investigated, as well as the chances and risks connected with implementation by the energy market partners.
Combining Different Tools for EEG Analysis to Study the Distributed Character of Language Processing
Directory of Open Access Journals (Sweden)
Armando Freitas da Rocha
Full Text Available Recent studies on language processing indicate that language cognition is better understood if assumed to be supported by a distributed intelligent processing system enrolling neurons located all over the cortex, in contrast to reductionism that proposes to localize cognitive functions to specific cortical structures. Here, brain activity was recorded using electroencephalogram while volunteers were listening or reading small texts and had to select pictures that translate meaning of these texts. Several techniques for EEG analysis were used to show this distributed character of neuronal enrollment associated with the comprehension of oral and written descriptive texts. Low Resolution Tomography identified the many different sets (si of neurons activated in several distinct cortical areas by text understanding. Linear correlation was used to calculate the information H(ei provided by each electrode of the 10/20 system about the identified si. H(ei Principal Component Analysis (PCA was used to study the temporal and spatial activation of these sources si. This analysis evidenced 4 different patterns of H(ei covariation that are generated by neurons located at different cortical locations. These results clearly show that the distributed character of language processing is clearly evidenced by combining available EEG technologies.
Study on Brain Dynamics by Non Linear Analysis of Music Induced EEG Signals
Banerjee, Archi; Sanyal, Shankha; Patranabis, Anirban; Banerjee, Kaushik; Guhathakurta, Tarit; Sengupta, Ranjan; Ghosh, Dipak; Ghose, Partha
Music has been proven to be a valuable tool for the understanding of human cognition, human emotion, and their underlying brain mechanisms. The objective of this study is to analyze the effect of Hindustani music on brain activity during normal relaxing conditions using electroencephalography (EEG). Ten male healthy subjects without special musical education participated in the study. EEG signals were acquired at the frontal (F3/F4) lobes of the brain while listening to music at three experimental conditions (rest, with music and without music). Frequency analysis was done for the alpha, theta and gamma brain rhythms. The finding shows that arousal based activities were enhanced while listening to Hindustani music of contrasting emotions (romantic/sorrow) for all the subjects in case of alpha frequency bands while no significant changes were observed in gamma and theta frequency ranges. It has been observed that when the music stimulus is removed, arousal activities as evident from alpha brain rhythms remain for some time, showing residual arousal. This is analogous to the conventional 'Hysteresis' loop where the system retains some 'memory' of the former state. This is corroborated in the non linear analysis (Detrended Fluctuation Analysis) of the alpha rhythms as manifested in values of fractal dimension. After an input of music conveying contrast emotions, withdrawal of music shows more retention as evidenced by the values of fractal dimension.
Spatiotemporal analysis of single-trial EEG of emotional pictures based on independent component analysis and source location
Liu, Jiangang; Tian, Jie
The present study combined the Independent Component Analysis (ICA) and low-resolution brain electromagnetic tomography (LORETA) algorithms to identify the spatial distribution and time course of single-trial EEG record differences between neural responses to emotional stimuli vs. the neutral. Single-trial multichannel (129-sensor) EEG records were collected from 21 healthy, right-handed subjects viewing the emotion emotional (pleasant/unpleasant) and neutral pictures selected from International Affective Picture System (IAPS). For each subject, the single-trial EEG records of each emotional pictures were concatenated with the neutral, and a three-step analysis was applied to each of them in the same way. First, the ICA was performed to decompose each concatenated single-trial EEG records into temporally independent and spatially fixed components, namely independent components (ICs). The IC associated with artifacts were isolated. Second, the clustering analysis classified, across subjects, the temporally and spatially similar ICs into the same clusters, in which nonparametric permutation test for Global Field Power (GFP) of IC projection scalp maps identified significantly different temporal segments of each emotional condition vs. neutral. Third, the brain regions accounted for those significant segments were localized spatially with LORETA analysis. In each cluster, a voxel-by-voxel randomization test identified significantly different brain regions between each emotional condition vs. the neutral. Compared to the neutral, both emotional pictures elicited activation in the visual, temporal, ventromedial and dorsomedial prefrontal cortex and anterior cingulated gyrus. In addition, the pleasant pictures activated the left middle prefrontal cortex and the posterior precuneus, while the unpleasant pictures activated the right orbitofrontal cortex, posterior cingulated gyrus and somatosensory region. Our results were well consistent with other functional imaging
Graph Theoretical Analysis of BOLD Functional Connectivity during Human Sleep without EEG Monitoring.
Directory of Open Access Journals (Sweden)
Jun Lv
Full Text Available Functional brain networks of human have been revealed to have small-world properties by both analyzing electroencephalogram (EEG and functional magnetic resonance imaging (fMRI time series.In our study, by using graph theoretical analysis, we attempted to investigate the changes of paralimbic-limbic cortex between wake and sleep states. Ten healthy young people were recruited to our experiment. Data from 2 subjects were excluded for the reason that they had not fallen asleep during the experiment. For each subject, blood oxygen level dependency (BOLD images were acquired to analyze brain network, and peripheral pulse signals were obtained continuously to identify if the subject was in sleep periods. Results of fMRI showed that brain networks exhibited stronger small-world characteristics during sleep state as compared to wake state, which was in consistent with previous studies using EEG synchronization. Moreover, we observed that compared with wake state, paralimbic-limbic cortex had less connectivity with neocortical system and centrencephalic structure in sleep.In conclusion, this is the first study, to our knowledge, has observed that small-world properties of brain functional networks altered when human sleeps without EEG synchronization. Moreover, we speculate that paralimbic-limbic cortex organization owns an efficient defense mechanism responsible for suppressing the external environment interference when humans sleep, which is consistent with the hypothesis that the paralimbic-limbic cortex may be functionally disconnected from brain regions which directly mediate their interactions with the external environment. Our findings also provide a reasonable explanation why stable sleep exhibits homeostasis which is far less susceptible to outside world.
FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data.
Oostenveld, Robert; Fries, Pascal; Maris, Eric; Schoffelen, Jan-Mathijs
This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.
A multi-dimensional functional principal components analysis of EEG data.
Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla
The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations. © 2017, The International Biometric Society.
Quantitative analysis of sleep EEG microstructure in the time-frequency domain.
De Carli, Fabrizio; Nobili, Lino; Beelke, Manolo; Watanabe, Tsuyoshi; Smerieri, Arianna; Parrino, Liborio; Terzano, Mario Giovanni; Ferrillo, Franco
A number of phasic events influence sleep quality and sleep macrostructure. The detection of arousals and the analysis of cyclic alternating patterns (CAP) support the evaluation of sleep fragmentation and instability. Sixteen polygraphic overnight recordings were visually inspected for conventional Rechtscaffen and Kales scoring, while arousals were detected following the criteria of the American Sleep Disorders Association (ASDA). Three electroencephalograph (EEG) segments were associated to each event, corresponding to background activity, pre-arousal period and arousal. The study was supplemented by the analysis of time-frequency distribution of EEG within each subtype of phase A in the CAP. The arousals were characterized by the increase of alpha and beta power with regard to background. Within NREM sleep most of the arousals were preceded by a transient increase of delta power. The time-frequency evolution of the phase A of the CAP sequence showed a strong prevalence of delta activity during the whole A1, but high amplitude delta waves were found also in the first 2/3 s of A2 and A3, followed by desynchronization. Our results underline the strict relationship between the ASDA arousals, and the subtype A2 and A3 within the CAP: in both the association between a short sequence of transient slow waves and the successive increase of frequency and decrease of amplitude characterizes the arousal response.
Group Independent Component Analysis (gICA) and Current Source Density (CSD) in the study of EEG in ADHD adults.
Ponomarev, Valery A; Mueller, Andreas; Candrian, Gian; Grin-Yatsenko, Vera A; Kropotov, Juri D
To investigate the performance of the spectral analysis of resting EEG, Current Source Density (CSD) and group independent components (gIC) in diagnosing ADHD adults. Power spectra of resting EEG, CSD and gIC (19 channels, linked ears reference, eyes open/closed) from 96 ADHD and 376 healthy adults were compared between eyes open and eyes closed conditions, and between groups of subjects. Pattern of differences in gIC and CSD spectral power between conditions was approximately similar, whereas it was more widely spatially distributed for EEG. Size effect (Cohen's d) of differences in gIC and CSD spectral power between groups of subjects was considerably greater than in the case of EEG. Significant reduction of gIC and CSD spectral power depending on conditions was found in ADHD patients. Reducing power in a wide frequency range in the fronto-central areas is a common phenomenon regardless of whether the eyes were open or closed. Spectral power of local EEG activity isolated by gICA or CSD in the fronto-central areas may be a suitable marker for discrimination of ADHD and healthy adults. Spectral analysis of gIC and CSD provides better sensitivity to discriminate ADHD and healthy adults. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications
Background Time-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention. As EEG is non-stationary, time-frequency analysis is essential to analyze brain states during different mental tasks. Further, the time-frequency information of EEG signal can be used as a feature for classification in brain-computer interface (BCI) applications. Methods To accurately model the EEG, band-limited multiple Fourier linear combiner (BMFLC), a linear combination of truncated multiple Fourier series models is employed. A state-space model for BMFLC in combination with Kalman filter/smoother is developed to obtain accurate adaptive estimation. By virtue of construction, BMFLC with Kalman filter/smoother provides accurate time-frequency decomposition of the bandlimited signal. Results The proposed method is computationally fast and is suitable for real-time BCI applications. To evaluate the proposed algorithm, a comparison with short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for both synthesized and real EEG data is performed in this paper. The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods. Conclusions Results show that the proposed algorithm can provide optimal time-frequency resolution as compared to STFT and CWT. For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement. PMID:24274109
Methods of stability analysis in nonlinear mechanics
International Nuclear Information System (INIS)
Warnock, R.L.; Ruth, R.D.; Gabella, W.; Ecklund, K.
We review our recent work on methods to study stability in nonlinear mechanics, especially for the problems of particle accelerators, and compare our ideals to those of other authors. We emphasize methods that (1) show promise as practical design tools, (2) are effective when the nonlinearity is large, and (3) have a strong theoretical basis. 24 refs., 2 figs., 2 tabs
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.
Delorme, Arnaud; Makeig, Scott
We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
Spectral theory and nonlinear analysis with applications to spatial ecology
Cano-Casanova, S; Mora-Corral , C
This volume details some of the latest advances in spectral theory and nonlinear analysis through various cutting-edge theories on algebraic multiplicities, global bifurcation theory, non-linear Schrödinger equations, non-linear boundary value problems, large solutions, metasolutions, dynamical systems, and applications to spatial ecology. The main scope of the book is bringing together a series of topics that have evolved separately during the last decades around the common denominator of spectral theory and nonlinear analysis - from the most abstract developments up to the most concrete applications to population dynamics and socio-biology - in an effort to fill the existing gaps between these fields.
Nonlinear finite element analysis of concrete structures
International Nuclear Information System (INIS)
Ottosen, N.S.
This report deals with nonlinear finite element analysis of concrete structures loaded in the short-term up until failure. A profound discussion of constitutive modelling on concrete is performed; a model, applicable for general stress states, is described and its predictions are compared with experimental data. This model is implemented in the AXIPLANE-program applicable for axisymmetrick and plane structures. The theoretical basis for this program is given. Using the AXIPLANE-program various concrete structures are analysed up until failure and compared with experimental evidence. These analyses include panels pressure vessel, beams failing in shear and finally a specific pull-out test, the Lok-Test, is considered. In these analyses, the influence of different failure criteria, aggregate interlock, dowel action, secondary cracking, magnitude of compressive strenght, magnitude of tensile strenght and of different post-failure behaviours of the concrete are evaluated. Moreover, it is shown that a suitable analysis of the theoretical data results in a clear insight into the physical behaviour of the considered structures. Finally, it is demonstrated that the AXISPLANE-program for widely different structures exhibiting very delicate structural aspects gives predictions that are in close agreement with experimental evidence. (author)
Connectivity maps based analysis of EEG for the advanced diagnosis of schizophrenia attributes.
Directory of Open Access Journals (Sweden)
Zack Dvey-Aharon
Full Text Available This article presents a novel connectivity analysis method that is suitable for multi-node networks such as EEG, MEG or EcOG electrode recordings. Its diagnostic power and ability to interpret brain states in schizophrenia is demonstrated on a set of 50 subjects that constituted of 25 healthy and 25 diagnosed with schizophrenia and treated with medication. The method can also be used for the automatic detection of schizophrenia; it exhibits higher sensitivity than state-of-the-art methods with no false positives. The detection is based on an analysis from a minute long pattern-recognition computer task. Moreover, this connectivity analysis leads naturally to an optimal choice of electrodes and hence to highly statistically significant results that are based on data from only 3-5 electrodes. The method is general and can be used for the diagnosis of other psychiatric conditions, provided an appropriate computer task is devised.
A Pilot Study of EEG Source Analysis Based Repetitive Transcranial Magnetic Stimulation for the Treatment of Tinnitus.
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Hui Wang
Full Text Available Repetitive Transcranial Magnetic Stimulation (rTMS is a novel therapeutic tool to induce a suppression of tinnitus. However, the optimal target sites are unknown. We aimed to determine whether low-frequency rTMS induced lasting suppression of tinnitus by decreasing neural activity in the cortex, navigated by high-density electroencephalogram (EEG source analysis, and the utility of EEG for targeting treatment.In this controlled three-armed trial, seven normal hearing patients with tonal tinnitus received a 10-day course of 1-Hz rTMS to the cortex, navigated by high-density EEG source analysis, to the left temporoparietal cortex region, and to the left temporoparietal with sham stimulation. The Tinnitus handicap inventory (THI and a visual analog scale (VAS were used to assess tinnitus severity and loudness. Measurements were taken before, and immediately, 2 weeks, and 4 weeks after the end of the interventions.Low-frequency rTMS decreased tinnitus significantly after active, but not sham, treatment. Responders in the EEG source analysis-based rTMS group, 71.4% (5/7 patients, experienced a significant reduction in tinnitus loudness, as evidenced by VAS scores. The target site of neuronal generators most consistently associated with a positive response was the frontal lobe in the right hemisphere, sourced using high-density EEG equipment, in the tinnitus patients. After left temporoparietal rTMS stimulation, 42.8% (3/7 patients experienced a decrease in tinnitus loudness.Active EEG source analysis based rTMS resulted in significant suppression in tinnitus loudness, showing the superiority of neuronavigation-guided coil positioning in dealing with tinnitus. Non-auditory areas should be considered in the pathophysiology of tinnitus. This knowledge in turn can contribute to investigate the pathophysiology of tinnitus.
Analysis and correction of ballistocardiogram contamination of EEG recordings in MR
International Nuclear Information System (INIS)
Jaeger, L.; Hoffmann, A.; Reiser, M.F.; Werhahn, K.J.
Purpose: to examine the influence of cardiac activity-related head movements and varying blood pulse frequencies on the shape of electroencephalography (EEG) recordings in a high magnetic field, and to implement a post-processing technique to eliminate cardiac activity-related artifacts. Material and methods: respiratory thoracic movements, changes of blood pulse frequency and passive head movements to 20 healthy subjects were examined outside and inside an MR magnet at rest in a simultaneously recorded 21-channel surface EEG. An electrocardiogram (ECG) was recorded simultaneously. On the basis of the correlation of the left ventricular ejection time (LVET) with the heart-rate, a post-processing heart-rate dependent subtraction of the cardiac activity-related artifacts of the EEG was developed. The quality of the post-processed EEG was tested by detecting alpha-activity in the pre- and post-processed EEGs. Results: inside the magnet, passive head motion but not respiratory thoracic movements resulted in EEG artifacts that correlated strongly with cardiac activity-related artifacts of the EEG. The blood pulse frequency influenced the appearance of the cardiac activity-related artifacts of the EEG. The removal of the cardiac activity-related artifacts of the EEG by the implemented post-processing algorithm resulted in an EEG of diagnostic quality with detected alpha-activity. Conclusion: when recording an EEG in MR environment, heart rate-dependent subtraction of EEG artifacts caused by ballistocardiogram contamination is essential to obtain EEG recordings of diagnostic quality and reliability. (orig.)
Analysis of Time n Frequency EEG Feature Extraction Methods for Mental Task Classification
Directory of Open Access Journals (Sweden)
Caglar Uyulan
Full Text Available Many endogenous and external components may affect the physiological, mental and behavioral states in humans. Monitoring tools are required to evaluate biomarkers, identify biological events, and predict their outcomes. Being one of the valuable indicators, brain biomarkers derived from temporal or spectral electroencephalography (EEG signals processing, allow for the classification of mental disorders and mental tasks. An EEG signal has a nonstationary nature and individual frequency feature, hence it can be concluded that each subject has peculiar timing and data to extract unique features. In order to classify data, which are collected by performing four mental task (reciting the alphabet backwards, imagination of rotation of a cube, imagination of right hand movements (open/close and performing mathematical operations, discriminative features were extracted using four competitive time-frequency techniques; Wavelet Packet Decomposition (WPD, Morlet Wavelet Transform (MWT, Short Time Fourier Transform (STFT and Wavelet Filter Bank (WFB, respectively. The extracted features using both time and frequency domain information were then reduced using a principal component analysis for subset reduction. Finally, the reduced subsets were fed into a multi-layer perceptron neural network (MP-NN trained with back propagation (BP algorithm to generate a predictive model. This study mainly focuses on comparing the relative performance of time-frequency feature extraction methods that are used to classify mental tasks. The real-time (RT conducted experimental results underlined that the WPD feature extraction method outperforms with 92% classification accuracy compared to three other aforementioned methods for four different mental tasks.
Comparative analysis of brain EEG signals generated from the right and left hand while writing
Sardesai, Neha; Jamali Mahabadi, S. E.; Meng, Qinglei; Choa, Fow-Sen
This paper provides a comparative analysis of right handed people and left handed people when they write with both their hands. Two left handed and one right handed subject were asked to write their respective names on a paper using both, their left and right handed, and their brain signals were measured using EEG. Similarly, they were asked to perform simple mathematical calculations using both their hand. The data collected from the EEG from writing with both hands is compared. It is observed that though it is expected that the right brain only would contribute to left handed writing and vice versa, it is not so. When a right handed person writes with his/her left hand, the initial instinct is to go for writing with the right hand. Hence, both parts of the brain are active when a subject writes with the other hand. However, when the activity is repeated, the brain learns to expect to write with the other hand as the activity is repeated and then only the expected part of the brain is active.
Nonlinear analysis and control of a continuous fermentation process
DEFF Research Database (Denmark)
Szederkényi, G.; Kristensen, Niels Rode; Hangos, K.M
Different types of nonlinear controllers are designed and compared for a simple continuous bioreactor operating near optimal productivity. This operating point is located close to a fold bifurcation point. Nonlinear analysis of stability, controllability and zero dynamics is used to investigate o...... are recommended for the simple fermenter. Passivity based controllers have been found to be globally stable, not very sensitive to the uncertainties in the reaction rate and controller parameter but they require full nonlinear state feedback....
Study on statistical analysis of nonlinear and nonstationary reactor noises
International Nuclear Information System (INIS)
Hayashi, Koji
For the purpose of identification of nonlinear mechanism and diagnosis of nuclear reactor systems, analysis methods for nonlinear reactor noise have been studied. By adding newly developed approximate response function to GMDH, a conventional nonlinear identification method, a useful method for nonlinear spectral analysis and identification of nonlinear mechanism has been established. Measurement experiment and analysis were performed on the reactor power oscillation observed in the NSRR installed at the JAERI and the cause of the instability was clarified. Furthermore, the analysis and data recording methods for nonstationary noise have been studied. By improving the time resolution of instantaneous autoregressive spectrum, a method for monitoring and diagnosis of operational status of nuclear reactor has been established. A preprocessing system for recording of nonstationary reactor noise was developed and its usability was demonstrated through a measurement experiment. (author) 139 refs
Nonlinear damage detection in composite structures using bispectral analysis
Ciampa, Francesco; Pickering, Simon; Scarselli, Gennaro; Meo, Michele
Literature offers a quantitative number of diagnostic methods that can continuously provide detailed information of the material defects and damages in aerospace and civil engineering applications. Indeed, low velocity impact damages can considerably degrade the integrity of structural components and, if not detected, they can result in catastrophic failure conditions. This paper presents a nonlinear Structural Health Monitoring (SHM) method, based on ultrasonic guided waves (GW), for the detection of the nonlinear signature in a damaged composite structure. The proposed technique, based on a bispectral analysis of ultrasonic input waveforms, allows for the evaluation of the nonlinear response due to the presence of cracks and delaminations. Indeed, such a methodology was used to characterize the nonlinear behaviour of the structure, by exploiting the frequency mixing of the original waveform acquired from a sparse array of sensors. The robustness of bispectral analysis was experimentally demonstrated on a damaged carbon fibre reinforce plastic (CFRP) composite panel, and the nonlinear source was retrieved with a high level of accuracy. Unlike other linear and nonlinear ultrasonic methods for damage detection, this methodology does not require any baseline with the undamaged structure for the evaluation of the nonlinear source, nor a priori knowledge of the mechanical properties of the specimen. Moreover, bispectral analysis can be considered as a nonlinear elastic wave spectroscopy (NEWS) technique for materials showing either classical or non-classical nonlinear behaviour.
Cross coherence independent component analysis in resting and action states EEG discrimination
International Nuclear Information System (INIS)
Almurshedi, A; Ismail, A K
Cross Coherence time frequency transform and independent component analysis (ICA) method were used to analyse the electroencephalogram (EEG) signals in resting and action states during open and close eyes conditions. From the topographical scalp distributions of delta, theta, alpha, and beta power spectrum can clearly discriminate between the signal when the eyes were open or closed, but it was difficult to distinguish between resting and action states when the eyes were closed. In open eyes condition, the frontal area (Fp1, Fp2) was activated (higher power) in delta and theta bands whilst occipital (O1, O2) and partial (P3, P4, Pz) area of brain was activated alpha band in closed eyes condition. The cross coherence method of time frequency analysis is capable of discrimination between rest and action brain signals in closed eyes condition
Analysis of Nonlinear Fractional Nabla Difference Equations
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Jagan Mohan Jonnalagadda
Full Text Available In this paper, we establish sufficient conditions on global existence and uniqueness of solutions of nonlinear fractional nabla difference systems and investigate the dependence of solutions on initial conditions and parameters.
Non-linear finite element analysis in structural mechanics
Rust, Wilhelm
This monograph describes the numerical analysis of non-linearities in structural mechanics, i.e. large rotations, large strain (geometric non-linearities), non-linear material behaviour, in particular elasto-plasticity as well as time-dependent behaviour, and contact. Based on that, the book treats stability problems and limit-load analyses, as well as non-linear equations of a large number of variables. Moreover, the author presents a wide range of problem sets and their solutions. The target audience primarily comprises advanced undergraduate and graduate students of mechanical and civil engineering, but the book may also be beneficial for practising engineers in industry.
Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor
Directory of Open Access Journals (Sweden)
Lin Ye
Full Text Available Using the finite element method (FEM and particle swarm optimization (PSO, a nonlinearity analysis based on parameter optimization is proposed to design an inductive angle sensor. Due to the structure complexity of the sensor, understanding the influences of structure parameters on the nonlinearity errors is a critical step in designing an effective sensor. Key parameters are selected for the design based on the parameters' effects on the nonlinearity errors. The finite element method and particle swarm optimization are combined for the sensor design to get the minimal nonlinearity error. In the simulation, the nonlinearity error of the optimized sensor is 0.053% in the angle range from −60° to 60°. A prototype sensor is manufactured and measured experimentally, and the experimental nonlinearity error is 0.081% in the angle range from −60° to 60°.
Stability analysis of embedded nonlinear predictor neural generalized predictive controller
Directory of Open Access Journals (Sweden)
Hesham F. Abdel Ghaffar
Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing's nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.
Nonlinear analysis of a reaction-diffusion system: Amplitude equations
Energy Technology Data Exchange (ETDEWEB)
Zemskov, E. P., E-mail: firstname.lastname@example.com [Russian Academy of Sciences, Dorodnicyn Computing Center (Russian Federation)
A reaction-diffusion system with a nonlinear diffusion term is considered. Based on nonlinear analysis, the amplitude equations are obtained in the cases of the Hopf and Turing instabilities in the system. Turing pattern-forming regions in the parameter space are determined for supercritical and subcritical instabilities in a two-component reaction-diffusion system.
Quantitative Analysis of the Effects of Slow Wave Sleep Deprivation During the First 3 h of Sleep on Subsequent EEG Power Density
Dijk, Derk Jan; Beersma, Domien G.M.; Daan, Serge; Bloem, Gerda M.; Hoofdakker, Rutger H. van den
The relation between EEG power density during slow wave sleep (SWS) deprivation and power density during subsequent sleep was investigated. Nine young male adults slept in the laboratory for 3 consecutive nights. Spectral analysis of the EEG on the 2nd (baseline) night revealed an exponential
Mutual information measures applied to EEG signals for sleepiness characterization.
Melia, Umberto; Guaita, Marc; Vallverdú, Montserrat; Embid, Cristina; Vilaseca, Isabel; Salamero, Manel; Santamaria, Joan
Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep latency (MSLT) tests alternated throughout the day from patients suffering from sleep disordered breathing. A group of 20 patients with excessive daytime sleepiness (EDS) was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60-s EEG windows in waking state. Measures obtained from cross-mutual information function (CMIF) and auto-mutual-information function (AMIF) were calculated in the EEG. These functions permitted a quantification of the complexity properties of the EEG signal and the non-linear couplings between different zones of the scalp. Statistical differences between EDS and WDS groups were found in β band during MSLT events (p-value CMIF measures yielded sensitivity and specificity above 80% and AUC of ROC above 0.85 in classifying EDS and WDS patients. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.
Nonlinear physical systems spectral analysis, stability and bifurcations
Kirillov, Oleg N
Bringing together 18 chapters written by leading experts in dynamical systems, operator theory, partial differential equations, and solid and fluid mechanics, this book presents state-of-the-art approaches to a wide spectrum of new and challenging stability problems.Nonlinear Physical Systems: Spectral Analysis, Stability and Bifurcations focuses on problems of spectral analysis, stability and bifurcations arising in the nonlinear partial differential equations of modern physics. Bifurcations and stability of solitary waves, geometrical optics stability analysis in hydro- and magnetohydrodynam
Discretization model for nonlinear dynamic analysis of three dimensional structures
International Nuclear Information System (INIS)
Hayashi, Y.
A discretization model for nonlinear dynamic analysis of three dimensional structures is presented. The discretization is achieved through a three dimensional spring-mass system and the dynamic response obtained by direct integration of the equations of motion using central diferences. First the viability of the model is verified through the analysis of homogeneous linear structures and then its performance in the analysis of structures subjected to impulsive or impact loads, taking into account both geometrical and physical nonlinearities is evaluated. (Author) [pt
Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution
Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin
Objective. In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). Approach. We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. Main results. Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6 ± 36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. Significance. Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.
Graph theoretical analysis of EEG effective connectivity in vascular dementia patients during a visual oddball task.
Wang, Chao; Xu, Jin; Zhao, Songzhen; Lou, Wutao
The study was dedicated to investigating the change in information processing in brain networks of vascular dementia (VaD) patients during the process of decision making. EEG was recorded from 18 VaD patients and 19 healthy controls when subjects were performing a visual oddball task. The whole task was divided into several stages by using global field power analysis. In the stage related to the decision-making process, graph theoretical analysis was applied to the binary directed network derived from EEG signals at nine electrodes in the frontal, central, and parietal regions in δ (0.5-3.5Hz), θ (4-7Hz), α1 (8-10Hz), α2 (11-13Hz), and β (14-30Hz) frequency bands based on directed transfer function. A weakened outgoing information flow, a decrease in out-degree, and an increase in in-degree were found in the parietal region in VaD patients, compared to healthy controls. In VaD patients, the parietal region may also lose its hub status in brain networks. In addition, the clustering coefficient was significantly lower in VaD patients. Impairment might be present in the parietal region or its connections with other regions, and it may serve as one of the causes for cognitive decline in VaD patients. The brain networks of VaD patients were significantly altered toward random networks. The present study extended our understanding of VaD from the perspective of brain functional networks, and it provided possible interpretations for cognitive deficits in VaD patients. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks
Gollas, Frank; Tetzlaff, Ronald
Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio
Spectral analysis of the EEG during halothane anaesthesia: Input-output relations
Silva, F.H. Lopes da; Smith, N. Ty; Zwart, Aart; Nichols, W.W.
1. 1. The "Halothane-brain compartment" system was investigated in dogs. The input was the inspired concentration of Halothane. The output was the intensity of EEG spectral components. The EEG was analysed by a hybrid system (analogue filters and digital integration in a small computer). For the
EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures
Wang, Lei; Long, Xi; Arends, J.B.A.M.; Aarts, R.M.
Background The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. New method A single-channel
Nonlinear Analysis of Cavities in Rock Salt
DEFF Research Database (Denmark)
Ottosen, N. S.; Krenk, Steen
The paper covers some material and computational aspects of the rock mechanics of leached cavities in salt. A material model is presented in which the instantaneous stiffness of the salt is obtained by interpolation between the unloaded state and a relevant failure state. The model enables predic...... prediction of short term triaxial behaviour from uniaxial stress-strain curves. Key results from a nonlinear finite element calculation of a gas-filled cavity are given, and the general features are related to a simple nonlinear method of stress evaluation....
A nonlinear analysis of the EHF booster
International Nuclear Information System (INIS)
Colton, E.P.; Shi, D.
We have analyzed particle motion at 1.2 GeV with assumption of nonlinearities arising from non-linear space charge forces and from the lattice sextupoles which are tuned to cancel the machine chromaticity. In the first case the motion is as expected and there are no problems as long as the x and y betatron tunes are separated by an integer or more. In the second case the motion is stable so long as the betatron amplitudes do not exceed values corresponding to beam normalized emittance of 100 mm-mr; when this occurs the effects of fifth-order betatron resonances are observed. 3 refs
Directory of Open Access Journals (Sweden)
H. Jafari
Full Text Available In this paper, nonlinear Klein-Gordon equation with quadratic term is solved by means of an analytic technique, namely the Homotopy analysis method (HAM.Comparisons are made between the Adomian decomposition method (ADM, the exact solution and homotopy analysis method. The results reveal that the proposed method is very effective and simple.
Nonlinear analysis on power reactor dynamics
International Nuclear Information System (INIS)
Konno, H.; Hayashi, K.
We have shown that the origin of intermittent oscillation observed in a BWR can be ascribed to the couplings among the spatial modes starting from a non-linear center manifold equation with a delay-time and a spatial diffusion. We can reduce the problem to the stochastic coupled van der Pol oscillators with non-linear coupling term. This non-linear coupling term plays an important role to break the symmetry of the system and the non-linear damping of the system. The phenomenological generalization of van der Pol oscillator coupled by the linear diffusion term is not appropriate for describing the nuclear power reactors. However, one must start from the coupled partial differential equations by taking into account the two energy group neutrons, the thermo-hydraulic equations including two-phase flow. In this case, the diffusion constant must be a complex number as is demonstrated in a previous paper. The results will be reported in the near future. (J.P.N.)
Data-driven analysis of simultaneous EEG/fMRI reveals neurophysiological phenotypes of impulse control.
Schmüser, Lena; Sebastian, Alexandra; Mobascher, Arian; Lieb, Klaus; Feige, Bernd; Tüscher, Oliver
Response inhibition is the ability to suppress inadequate but prepotent or ongoing response tendencies. A fronto-striatal network is involved in these processes. Between-subject differences in the intra-individual variability have been suggested to constitute a key to pathological processes underlying impulse control disorders. Single-trial EEG/fMRI analysis allows to increase sensitivity for inter-individual differences by incorporating intra-individual variability. Thirty-eight healthy subjects performed a visual Go/Nogo task during simultaneous EEG/fMRI. Of 38 healthy subjects, 21 subjects reliably showed Nogo-related ICs (Nogo-IC-positive) while 17 subjects (Nogo-IC-negative) did not. Comparing both groups revealed differences on various levels: On trait level, Nogo-IC-negative subjects scored higher on questionnaires regarding attention deficit/hyperactivity disorder; on a behavioral level, they displayed slower response times (RT) and higher intra-individual RT variability while both groups did not differ in their inhibitory performance. On the neurophysiological level, Nogo-IC-negative subjects showed a hyperactivation of left inferior frontal cortex/insula and left putamen as well as significantly reduced P3 amplitudes. Thus, a data-driven approach for IC classification and the resulting presence or absence of early Nogo-specific ICs as criterion for group selection revealed group differences at behavioral and neurophysiological levels. This may indicate electrophysiological phenotypes characterized by inter-individual variations of neural and behavioral correlates of impulse control. We demonstrated that the inter-individual difference in an electrophysiological correlate of response inhibition is correlated with distinct, potentially compensatory neural activity. This may suggest the existence of electrophysiologically dissociable phenotypes of behavioral and neural motor response inhibition with the Nogo-IC-positive phenotype possibly providing
Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory
Directory of Open Access Journals (Sweden)
van der Flier Wiesje M
Full Text Available Abstract Background Although a large body of knowledge about both brain structure and function has been gathered over the last decades, we still have a poor understanding of their exact relationship. Graph theory provides a method to study the relation between network structure and function, and its application to neuroscientific data is an emerging research field. We investigated topological changes in large-scale functional brain networks in patients with Alzheimer's disease (AD and frontotemporal lobar degeneration (FTLD by means of graph theoretical analysis of resting-state EEG recordings. EEGs of 20 patients with mild to moderate AD, 15 FTLD patients, and 23 non-demented individuals were recorded in an eyes-closed resting-state. The synchronization likelihood (SL, a measure of functional connectivity, was calculated for each sensor pair in 0.5–4 Hz, 4–8 Hz, 8–10 Hz, 10–13 Hz, 13–30 Hz and 30–45 Hz frequency bands. The resulting connectivity matrices were converted to unweighted graphs, whose structure was characterized with several measures: mean clustering coefficient (local connectivity, characteristic path length (global connectivity and degree correlation (network 'assortativity'. All results were normalized for network size and compared with random control networks. Results In AD, the clustering coefficient decreased in the lower alpha and beta bands (p Conclusion With decreasing local and global connectivity parameters, the large-scale functional brain network organization in AD deviates from the optimal 'small-world' network structure towards a more 'random' type. This is associated with less efficient information exchange between brain areas, supporting the disconnection hypothesis of AD. Surprisingly, FTLD patients show changes in the opposite direction, towards a (perhaps excessively more 'ordered' network structure, possibly reflecting a different underlying pathophysiological process.
Temporal dynamics of sensorimotor integration in speech perception and production: Independent component analysis of EEG data
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David eJenson
Full Text Available Activity in premotor and sensorimotor cortices is found in speech production and some perception tasks. Yet, how sensorimotor integration supports these functions is unclear due to a lack of data examining the timing of activity from these regions. Beta (~20Hz and alpha (~10Hz spectral power within the EEG µ rhythm are considered indices of motor and somatosensory activity, respectively. In the current study, perception conditions required discrimination (same/different of syllables pairs (/ba/ and /da/ in quiet and noisy conditions. Production conditions required covert and overt syllable productions and overt word production. Independent component analysis was performed on EEG data obtained during these conditions to 1 identify clusters of µ components common to all conditions and 2 examine real-time event-related spectral perturbations (ERSP within alpha and beta bands. 17 and 15 out of 20 participants produced left and right µ-components, respectively, localized to precentral gyri. Discrimination conditions were characterized by significant (pFDR<.05 early alpha event-related synchronization (ERS prior to and during stimulus presentation and later alpha event-related desynchronization (ERD following stimulus offset. Beta ERD began early and gained strength across time. Differences were found between quiet and noisy discrimination conditions. Both overt syllable and word productions yielded similar alpha/beta ERD that began prior to production and was strongest during muscle activity. Findings during covert production were weaker than during overt production. One explanation for these findings is that µ-beta ERD indexes early predictive coding (e.g., internal modeling and/or overt and covert attentional / motor processes. µ-alpha ERS may index inhibitory input to the premotor cortex from sensory regions prior to and during discrimination, while µ-alpha ERD may index re-afferent sensory feedback during speech rehearsal and production.
Frequency domain performance analysis of nonlinearly controlled motion systems
Pavlov, A.V.; Wouw, van de N.; Pogromski, A.Y.; Heertjes, M.F.; Nijmeijer, H.
At the heart of the performance analysis of linear motion control systems lie essential frequency domain characteristics such as sensitivity and complementary sensitivity functions. For a class of nonlinear motion control systems called convergent systems, generalized versions of these sensitivity
Linear and nonlinear analysis of high-power rf amplifiers
International Nuclear Information System (INIS)
Puglisi, M.
After a survey of the state variable analysis method the final amplifier for the CBA is analyzed taking into account the real beam waveshape. An empirical method for checking the stability of a non-linear system is also considered
Nonlinear seismic analysis of a large sodium pump
International Nuclear Information System (INIS)
Huang, S.N.
The bearings and seismic bumpers used in a large sodium pump of a typical breeder reactor plant may need to be characterized by nonlinear springs and gaps. Then, nonlinear seismic analysis utilizing the time-history method is an effective way to predict the pump behaviors during seismic events - especially at those bearing and seismic bumper areas. In this study, synthesized time histories were developed based on specified seismic response spectra. A nonlinear seismic analysis was then conducted and results were compared with those obtained by linear seismic analysis using the response spectrum method. In contrast to some previous nonlinear analysis trends, the bearing impact forces predicted by nonlinear analysis were higher than those obtained by the response spectrum method. This might be due to the larger gaps and stiffer bearing supports used in this specific pump. However, at locations distant from the impact source, the nonlinear seismic analysis has predicted slightly less responses than those obtained by linear seismic analysis. The seismically induced bearing impact forces were used to study the friction induced thermal stresses on the hydrostatic bearing and to predict the coastdown time of the pump. Results and discussions are presented
Nonlinear seismic analysis of a large sodium pump
International Nuclear Information System (INIS)
Huang, S.N.
The bearings and seismic bumpers used in a large sodium pump of a typical breeder reactor plant may need to be characterized by nonlinear springs and gaps. Then, nonlinear seismic analysis utilizing the time-history method is an effective way to predict the pump behaviors during seismic events, especially at those bearing and seismic bumper areas. In this study, synthesized time histories were developed based on specified seismic response spectra. A nonlinear seismic analysis was then conducted and results were compared with those obtained by linear seismic analysis using the response spectrum method. In contrast to some previous nonlinear analysis trends, the bearing impact forces predicted by nonlinear analysis were higher than those obtained by the response spectrum method. This might be due to the larger gaps and stiffer bearing supports used in this specific pump. However, at locations distant from the impact source, the nonlinear seismic analysis has predicted slightly less responses than those obtained by linear seismic analysis. The seismically induced bearing impact forces were used to study the friction induced thermal stresses on the hydrostatic bearing and to predict the coastdown time of the pump. Results and discussions are presented
Nonlinear Finite Strain Consolidation Analysis with Secondary Consolidation Behavior
Directory of Open Access Journals (Sweden)
Jieqing Huang
Full Text Available This paper aims to analyze nonlinear finite strain consolidation with secondary consolidation behavior. On the basis of some assumptions about the secondary consolidation behavior, the continuity equation of pore water in Gibson's consolidation theory is modified. Taking the nonlinear compressibility and nonlinear permeability of soils into consideration, the governing equation for finite strain consolidation analysis is derived. Based on the experimental data of Hangzhou soft clay samples, the new governing equation is solved with the finite element method. Afterwards, the calculation results of this new method and other two methods are compared. It can be found that Gibson's method may underestimate the excess pore water pressure during primary consolidation. The new method which takes the secondary consolidation behavior, the nonlinear compressibility, and nonlinear permeability of soils into consideration can precisely estimate the settlement rate and the final settlement of Hangzhou soft clay sample.
Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients' Consciousness Level Based on Anesthesiologists Experience
Directory of Open Access Journals (Sweden)
George J. A. Jiang
Full Text Available Electroencephalogram (EEG signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA. Bispectral (BIS index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD method and analyzed using sample entropy (SampEn analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN model through using expert assessment of consciousness level (EACL which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.
Feature analysis for correlation studies of simultaneous EEG-fMRI data: A proof of concept for neurofeedback approaches.
Simoes, Simões; Lima, João; Direito, Bruno; Castelhano, João; Ferreira, Carlos; Carvalho, Paulo; Castelo-Branco, Miguel
The identification and interpretation of facial expressions is an important feature of social cognition. This characteristic is often impaired in various neurodevelopmental disorders. Recent therapeutic approaches to intervene in social communication impairments include neurofeedback (NF). In this study, we present a NF real-time functional Magnetic Resonance Imaging (rt-fMRI), combined with electroencephalography (EEG) to train social communication skills. In this sense, we defined the right Superior Temporal Sulcus as our target region-of-interest. To analyze the correlation between the fMRI regions of interest and the EEG data, we transposed the sources located at the nearest cortical location to the target region. We extracted a set of 75 features from EEG segments and performed a correlation analysis with the brain activations extracted from rt-fMRI in the right pSTS region. The finding of significant correlations of simultaneously measured signals in distinct modalities (EEG and fMRI) is promising. Future studies should address whether the observed correlation levels between local brain activity and scalp measures are sufficient to implement NF approaches.
Nonlinear Analysis of Renal Autoregulation Under Broadband Forcing Conditions
DEFF Research Database (Denmark)
Marmarelis, V Z; Chon, K H; Chen, Y M
Linear analysis of renal blood flow fluctuations, induced experimentally in rats by broad-band (pseudorandom) arterial blood pressure forcing at various power levels, has been unable to explain fully the dynamics of renal autoregulation at low frequencies. This observation has suggested...... the possibility of nonlinear mechanisms subserving renal autoregulation at frequencies below 0.2 Hz. This paper presents results of 3rd-order Volterra-Wiener analysis that appear to explain adequately the nonlinearities in the pressure-flow relation below 0.2 Hz in rats. The contribution of the 3rd-order kernel...... in describing the dynamic pressure-flow relation is found to be important. Furthermore, the dependence of 1st-order kernel waveforms on the power level of broadband pressure forcing indicates the presence of nonlinear feedback (of sigmoid type) based on previously reported analysis of a class of nonlinear...
Nonlinear analysis of RED - a comparative study
International Nuclear Information System (INIS)
Jiang Kai; Wang Xiaofan; Xi Yugeng
Random Early Detection (RED) is an active queue management (AQM) mechanism for routers on the Internet. In this paper, performance of RED and Adaptive RED are compared from the viewpoint of nonlinear dynamics. In particular, we reveal the relationship between the performance of the network and its nonlinear dynamical behavior. We measure the maximal Lyapunov exponent and Hurst parameter of the average queue length of RED and Adaptive RED, as well as the throughput and packet loss rate of the aggregate traffic on the bottleneck link. Our simulation scenarios include FTP flows and Web flows, one-way and two-way traffic. In most situations, Adaptive RED has smaller maximal Lyapunov exponents, lower Hurst parameters, higher throughput and lower packet loss rate than that of RED. This confirms that Adaptive RED has better performance than RED
Nonlinear analysis of RED - a comparative study
Energy Technology Data Exchange (ETDEWEB)
Jiang Kai; Wang Xiaofan E-mail: firstname.lastname@example.com; Xi Yugeng
Random Early Detection (RED) is an active queue management (AQM) mechanism for routers on the Internet. In this paper, performance of RED and Adaptive RED are compared from the viewpoint of nonlinear dynamics. In particular, we reveal the relationship between the performance of the network and its nonlinear dynamical behavior. We measure the maximal Lyapunov exponent and Hurst parameter of the average queue length of RED and Adaptive RED, as well as the throughput and packet loss rate of the aggregate traffic on the bottleneck link. Our simulation scenarios include FTP flows and Web flows, one-way and two-way traffic. In most situations, Adaptive RED has smaller maximal Lyapunov exponents, lower Hurst parameters, higher throughput and lower packet loss rate than that of RED. This confirms that Adaptive RED has better performance than RED.
Nonlinear analysis of ring oscillator circuits
Ge, Xiaoqing
Using nonlinear systems techniques, we analyze the stability properties and synchronization conditions for ring oscillator circuits, which are essential building blocks in digital systems. By making use of its cyclic structure, we investigate local and global stability properties of an n-stage ring oscillator. We present a sufficient condition for global asymptotic stability of the origin and obtain necessity if the ring oscillator consists of identical inverter elements. We then give a synchronization condition for identical interconnected ring oscillators.
Nonlinear analysis of ring oscillator circuits
Ge, Xiaoqing; Arcak, Murat; Salama, Khaled N.
Using nonlinear systems techniques, we analyze the stability properties and synchronization conditions for ring oscillator circuits, which are essential building blocks in digital systems. By making use of its cyclic structure, we investigate local and global stability properties of an n-stage ring oscillator. We present a sufficient condition for global asymptotic stability of the origin and obtain necessity if the ring oscillator consists of identical inverter elements. We then give a synchronization condition for identical interconnected ring oscillators.
Analysis and design of robust decentralized controllers for nonlinear systems
Energy Technology Data Exchange (ETDEWEB)
Schoenwald, D.A.
Decentralized control strategies for nonlinear systems are achieved via feedback linearization techniques. New results on optimization and parameter robustness of non-linear systems are also developed. In addition, parametric uncertainty in large-scale systems is handled by sensitivity analysis and optimal control methods in a completely decentralized framework. This idea is applied to alleviate uncertainty in friction parameters for the gimbal joints on Space Station Freedom. As an example of decentralized nonlinear control, singular perturbation methods and distributed vibration damping are merged into a control strategy for a two-link flexible manipulator.
The relationship between structural and functional connectivity: graph theoretical analysis of an EEG neural mass model
Ponten, S.C.; Daffertshofer, A.; Hillebrand, A.; Stam, C.J.
We investigated the relationship between structural network properties and both synchronization strength and functional characteristics in a combined neural mass and graph theoretical model of the electroencephalogram (EEG). Thirty-two neural mass models (NMMs), each representing the lump activity
NOLB: Nonlinear Rigid Block Normal Mode Analysis Method
Hoffmann , Alexandre; Grudinin , Sergei
International audience; We present a new conceptually simple and computationally efficient method for nonlinear normal mode analysis called NOLB. It relies on the rotations-translations of blocks (RTB) theoretical basis developed by Y.-H. Sanejouand and colleagues. We demonstrate how to physically interpret the eigenvalues computed in the RTB basis in terms of angular and linear velocities applied to the rigid blocks and how to construct a nonlinear extrapolation of motion out of these veloci...
A Novel Analysis of Performance Classification and Workload Prediction Using Electroencephalography (EEG) Frequency Data
calming music to ease the individual before the start of the study [8]. EEG data contains noise ranging from muscle twitches, blinking and other functions...depict brain activity visually, Borghini et al was also able to note the trend of the supposed learning process using only the Theta EEG frequency...named Prediction of Operator Performance ( POP ). One of the assumptions of this model is that only a small number of cognitive activities can be
Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain.
Giacometti, Paolo; Perdue, Katherine L; Diamond, Solomon G
Interpretation and analysis of electroencephalography (EEG) measurements relies on the correspondence of electrode scalp coordinates to structural and functional regions of the brain. An algorithm is introduced for automatic calculation of the International 10-20, 10-10, and 10-5 scalp coordinates of EEG electrodes on a boundary element mesh of a human head. The EEG electrode positions are then used to generate parcellation regions of the cerebral cortex based on proximity to the EEG electrodes. The scalp electrode calculation method presented in this study effectively and efficiently identifies EEG locations without prior digitization of coordinates. The average of electrode proximity parcellations of the cortex were tabulated with respect to structural and functional regions of the brain in a population of 20 adult subjects. Parcellations based on electrode proximity and EEG sensitivity were compared. The parcellation regions based on sensitivity and proximity were found to have 44.0 ± 11.3% agreement when demarcated by the International 10-20, 32.4 ± 12.6% by the 10-10, and 24.7 ± 16.3% by the 10-5 electrode positioning system. The EEG positioning algorithm is a fast and easy method of locating EEG scalp coordinates without the need for digitized electrode positions. The parcellation method presented summarizes the EEG scalp locations with respect to brain regions without computation of a full EEG forward model solution. The reference table of electrode proximity versus cortical regions may be used by experimenters to select electrodes that correspond to anatomical and functional regions of interest. Copyright © 2014 Elsevier B.V. All rights reserved.
Seismic analysis of a nonlinear airlock system
International Nuclear Information System (INIS)
Huang, S.N.
The containment equipment airlock door of the Fast Flux Test Facility utilizes screw-type actuators as a push-pull mechanism for closing and opening operations. Special design features were used to protect these actuators from pressure differential loading. These made the door behave as a nonlinear system during a seismic event. Seismic analyses, utilizing the time history method, were conducted to determine the seismic loads on these scew-type actuators. Several sizes of actuators were examined. Procedures for determining the final optimum design are discussed in detail
Similar speaker recognition using nonlinear analysis
International Nuclear Information System (INIS)
Seo, J.P.; Kim, M.S.; Baek, I.C.; Kwon, Y.H.; Lee, K.S.; Chang, S.W.; Yang, S.I.
Speech features of the conventional speaker identification system, are usually obtained by linear methods in spectral space. However, these methods have the drawback that speakers with similar voices cannot be distinguished, because the characteristics of their voices are also similar in spectral space. To overcome the difficulty in linear methods, we propose to use the correlation exponent in the nonlinear space as a new feature vector for speaker identification among persons with similar voices. We show that our proposed method surprisingly reduces the error rate of speaker identification system to speakers with similar voices
... in diagnosing brain disorders, especially epilepsy or another seizure disorder. An EEG might also be helpful for diagnosing ... Sometimes seizures are intentionally triggered in people with epilepsy during the test, but appropriate medical care is ...
Detecting Nonlinear Oscillations in Broadband Signals
Czech Academy of Sciences Publication Activity Database
Vejmelka, Martin; Paluš, Milan
Roč. 19, - (2009), 1015114-1-1015114-7 ISSN 1054-1500 R&D Projects: GA MŠk 7E08027 EU Projects: European Commission(XE) 200728 - BRAINSYNC Institutional research plan: CEZ:AV0Z10300504 Keywords : nonlinear dynamical systems * oscillations * random processes * time series analysis * EEG Subject RIV: FH - Neurology Impact factor: 1.795, year: 2009
BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis.
Kleifges, Kelly; Bigdely-Shamlo, Nima; Kerick, Scott E; Robbins, Kay A
Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/.
Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively.
Shimamoto, Shoichi; Waldman, Zachary J; Orosz, Iren; Song, Inkyung; Bragin, Anatol; Fried, Itzhak; Engel, Jerome; Staba, Richard; Sharan, Ashwini; Wu, Chengyuan; Sperling, Michael R; Weiss, Shennan A
To develop and validate a detector that identifies ripple (80-200 Hz) events in intracranial EEG (iEEG) recordings in a referential montage and utilizes independent component analysis (ICA) to eliminate or reduce high-frequency artifact contamination. Also, investigate the correspondence of detected ripples and the seizure onset zone (SOZ). iEEG recordings from 16 patients were first band-pass filtered (80-600 Hz) and Infomax ICA was next applied to derive the first independent component (IC1). IC1 was subsequently pruned, and an artifact index was derived to reduce the identification of high-frequency events introduced by the reference electrode signal. A Hilbert detector identified ripple events in the processed iEEG recordings using amplitude and duration criteria. The identified ripple events were further classified and characterized as true or false ripple on spikes, or ripples on oscillations by utilizing a topographical analysis to their time-frequency plot, and confirmed by visual inspection. The signal to noise ratio was improved by pruning IC1. The precision of the detector for ripple events was 91.27 ± 4.3%, and the sensitivity of the detector was 79.4 ± 3.0% (N = 16 patients, 5842 ripple events). The sensitivity and precision of the detector was equivalent in iEEG recordings obtained during sleep or intra-operatively. Across all the patients, true ripple on spike rates and also the rates of false ripple on spikes, that were generated due to filter ringing, classified the seizure onset zone (SOZ) with an area under the receiver operating curve (AUROC) of >76%. The magnitude and spectral content of true ripple on spikes generated in the SOZ was distinct as compared with the ripples generated in the NSOZ (p ripple rates and properties defined using this approach may accurately delineate the seizure onset zone. Strategies to improve the spatial resolution of intracranial EEG and reduce artifact can help improve the clinical utility of
Nonlinear dynamic analysis of flexible multibody systems
Bauchau, Olivier A.; Kang, Nam Kook
Two approaches are developed to analyze the dynamic behavior of flexible multibody systems. In the first approach each body is modeled with a modal methodology in a local non-inertial frame of reference, whereas in the second approach, each body is modeled with a finite element methodology in the inertial frame. In both cases, the interaction among the various elastic bodies is represented by constraint equations. The two approaches were compared for accuracy and efficiency: the first approach is preferable when the nonlinearities are not too strong but it becomes cumbersome and expensive to use when many modes must be used. The second approach is more general and easier to implement but could result in high computation costs for a large system. The constraints should be enforced in a time derivative fashion for better accuracy and stability.
Nonlinear MHD analysis for LHD plasmas
International Nuclear Information System (INIS)
Ichiguchi, K.; Nakajima, N.; Wakatani, M.; Carreras, B.A.
The nonlinear behavior of the interchange modes with multi-helicity in the Large Helical Device is analyzed based on the reduced MHD equations. In the equilibrium at sufficiently low beta value, the saturation of a single mode and the following excitation of other single mode whose resonant surface is close to that of the saturated mode are slowly repeated. This sequence leads to the local deformation of the pressure profile. Increasing the beta value with the pressure profile fixed, a bursting phenomenon due to the overlap of multiple modes is observed in the kinetic energy, which results in the global reduction of the pressure profile. Increasing the beta value using the pressure profile saturated at the lower beta value suppresses the bursting behavior. This result indicates the possibility that the pressure profile is self-organized so that the LHD plasma should attain the high beta regime through a stable path. (author)
The Photoplethismographic Signal Processed with Nonlinear Time Series Analysis Tools
International Nuclear Information System (INIS)
Hernandez Caceres, Jose Luis; Hong, Rolando; Garcia Lanz, Abel; Garcia Dominguez, Luis; Cabannas, Karelia
Finger photoplethismography (PPG) signals were submitted to nonlinear time series analysis. The applied analytical techniques were: (i) High degree polynomial fitting for baseline estimation; (ii) FFT analysis for estimating power spectra; (iii) fractal dimension estimation via the Higuchi's time-domain method, and (iv) kernel nonparametric estimation for reconstructing noise free-attractors and also for estimating signal's stochastic components
Nonlinear Redundancy Analysis. Research Report 88-1.
van der Burg, Eeke; de Leeuw, Jan
A non-linear version of redundancy analysis is introduced. The technique is called REDUNDALS. It is implemented within the computer program for canonical correlation analysis called CANALS. The REDUNDALS algorithm is of an alternating least square (ALS) type. The technique is defined as minimization of a squared distance between criterion…
Nonlinear time series analysis of the human electrocardiogram
International Nuclear Information System (INIS)
Perc, Matjaz
We analyse the human electrocardiogram with simple nonlinear time series analysis methods that are appropriate for graduate as well as undergraduate courses. In particular, attention is devoted to the notions of determinism and stationarity in physiological data. We emphasize that methods of nonlinear time series analysis can be successfully applied only if the studied data set originates from a deterministic stationary system. After positively establishing the presence of determinism and stationarity in the studied electrocardiogram, we calculate the maximal Lyapunov exponent, thus providing interesting insights into the dynamics of the human heart. Moreover, to facilitate interest and enable the integration of nonlinear time series analysis methods into the curriculum at an early stage of the educational process, we also provide user-friendly programs for each implemented method
Fast automatic analysis of antenatal dexamethasone on micro-seizure activity in the EEG
International Nuclear Information System (INIS)
Rastin, S.J.; Unsworth, C.P.; Bennet, L.
Full text: In this work wc develop an automatic scheme for studying the effect of the antenatal Dexamethasone on the EEG activity. To do so an FFT (Fast Fourier Transform) based detector was designed and applied to the EEG recordings obtained from two groups of fetal sheep. Both groups received two injections with a time delay of 24 h between them. However the applied medicine was different for each group (Dex and saline). The detector developed was used to automatically identify and classify micro-seizures that occurred in the frequency bands corresponding to the EEG transients known as slow waves (2.5 14 Hz). For each second of the data recordings the spectrum was computed and the rise of the energy in each predefined frequency band then counted when the energy level exceeded a predefined corresponding threshold level (Where the threshold level was obtained from the long term average of the spectral points at each band). Our results demonstrate that it was possible to automatically count the micro-seizures for the three different bands in a time effective manner. It was found that the number of transients did not strongly depend on the nature of the injected medicine which was consistent with the results manually obtained by an EEG expert. Tn conclusion, the automatic detection scheme presented here would allow for rapid micro-seizure event identification of hours of highly sampled EEG data thus providing a valuable time-saving device.
Linear and nonlinear subspace analysis of hand movements during grasping.
Cui, Phil Hengjun; Visell, Yon
This study investigated nonlinear patterns of coordination, or synergies, underlying whole-hand grasping kinematics. Prior research has shed considerable light on roles played by such coordinated degrees-of-freedom (DOF), illuminating how motor control is facilitated by structural and functional specializations in the brain, peripheral nervous system, and musculoskeletal system. However, existing analyses suppose that the patterns of coordination can be captured by means of linear analyses, as linear combinations of nominally independent DOF. In contrast, hand kinematics is itself highly nonlinear in nature. To address this discrepancy, we sought to to determine whether nonlinear synergies might serve to more accurately and efficiently explain human grasping kinematics than is possible with linear analyses. We analyzed motion capture data acquired from the hands of individuals as they grasped an array of common objects, using four of the most widely used linear and nonlinear dimensionality reduction algorithms. We compared the results using a recently developed algorithm-agnostic quality measure, which enabled us to assess the quality of the dimensional reductions that resulted by assessing the extent to which local neighborhood information in the data was preserved. Although qualitative inspection of this data suggested that nonlinear correlations between kinematic variables were present, we found that linear modeling, in the form of Principle Components Analysis, could perform better than any of the nonlinear techniques we applied.
Nonlinear Resonance Analysis of Slender Portal Frames under Base Excitation
Directory of Open Access Journals (Sweden)
Luis Fernando Paullo Muñoz
Full Text Available The dynamic nonlinear response and stability of slender structures in the main resonance regions are a topic of importance in structural analysis. In complex problems, the determination of the response in the frequency domain indirectly obtained through analyses in time domain can lead to huge computational effort in large systems. In nonlinear cases, the response in the frequency domain becomes even more cumbersome because of the possibility of multiple solutions for certain forcing frequencies. Those solutions can be stable and unstable, in particular saddle-node bifurcation at the turning points along the resonance curves. In this work, an incremental technique for direct calculation of the nonlinear response in frequency domain of plane frames subjected to base excitation is proposed. The transformation of equations of motion to the frequency domain is made through the harmonic balance method in conjunction with the Galerkin method. The resulting system of nonlinear equations in terms of the modal amplitudes and forcing frequency is solved by the Newton-Raphson method together with an arc-length procedure to obtain the nonlinear resonance curves. Suitable examples are presented, and the influence of the frame geometric parameters and base motion on the nonlinear resonance curves is investigated.
Nonlinear analysis of NPP safety against the aircraft attack
International Nuclear Information System (INIS)
Králik, Juraj; Králik, Juraj
The paper presents the nonlinear probabilistic analysis of the reinforced concrete buildings of nuclear power plant under the aircraft attack. The dynamic load is defined in time on base of the airplane impact simulations considering the real stiffness, masses, direction and velocity of the flight. The dynamic response is calculated in the system ANSYS using the transient nonlinear analysis solution method. The damage of the concrete wall is evaluated in accordance with the standard NDRC considering the spalling, scabbing and perforation effects. The simple and detailed calculations of the wall damage are compared.
Nonlinear analysis of NPP safety against the aircraft attack
Energy Technology Data Exchange (ETDEWEB)
Králik, Juraj, E-mail: email@example.com [Faculty of Civil Engineering, STU in Bratislava, Radlinského 11, 813 68 Bratislava (Slovakia); Králik, Juraj, E-mail: firstname.lastname@example.com [Faculty of Architecture, STU in Bratislava, Námestie Slobody 19, 812 45 Bratislava (Slovakia)
The paper presents the nonlinear probabilistic analysis of the reinforced concrete buildings of nuclear power plant under the aircraft attack. The dynamic load is defined in time on base of the airplane impact simulations considering the real stiffness, masses, direction and velocity of the flight. The dynamic response is calculated in the system ANSYS using the transient nonlinear analysis solution method. The damage of the concrete wall is evaluated in accordance with the standard NDRC considering the spalling, scabbing and perforation effects. The simple and detailed calculations of the wall damage are compared.
Nonlinear Analysis and Modeling of Tires
Noor, Ahmed K.
The objective of the study was to develop efficient modeling techniques and computational strategies for: (1) predicting the nonlinear response of tires subjected to inflation pressure, mechanical and thermal loads; (2) determining the footprint region, and analyzing the tire pavement contact problem, including the effect of friction; and (3) determining the sensitivity of the tire response (displacements, stresses, strain energy, contact pressures and contact area) to variations in the different material and geometric parameters. Two computational strategies were developed. In the first strategy the tire was modeled by using either a two-dimensional shear flexible mixed shell finite elements or a quasi-three-dimensional solid model. The contact conditions were incorporated into the formulation by using a perturbed Lagrangian approach. A number of model reduction techniques were applied to substantially reduce the number of degrees of freedom used in describing the response outside the contact region. The second strategy exploited the axial symmetry of the undeformed tire, and uses cylindrical coordinates in the development of three-dimensional elements for modeling each of the different parts of the tire cross section. Model reduction techniques are also used with this strategy.
Nonlinear Progressive Collapse Analysis Including Distributed Plasticity
Directory of Open Access Journals (Sweden)
Mohamed Osama Ahmed
Full Text Available This paper demonstrates the effect of incorporating distributed plasticity in nonlinear analytical models used to assess the potential for progressive collapse of steel framed regular building structures. Emphasis on this paper is on the deformation response under the notionally removed column, in a typical Alternate Path (AP method. The AP method employed in this paper is based on the provisions of the Unified Facilities Criteria – Design of Buildings to Resist Progressive Collapse, developed and updated by the U.S. Department of Defense [1]. The AP method is often used for to assess the potential for progressive collapse of building structures that fall under Occupancy Category III or IV. A case study steel building is used to examine the effect of incorporating distributed plasticity, where moment frames were used on perimeter as well as the interior of the three dimensional structural system. It is concluded that the use of moment resisting frames within the structural system will enhance resistance to progressive collapse through ductile deformation response and that it is conserative to ignore the effects of distributed plasticity in determining peak displacement response under the notionally removed column.
Discretization analysis of bifurcation based nonlinear amplifiers
Feldkord, Sven; Reit, Marco; Mathis, Wolfgang
Recently, for modeling biological amplification processes, nonlinear amplifiers based on the supercritical Andronov-Hopf bifurcation have been widely analyzed analytically. For technical realizations, digital systems have become the most relevant systems in signal processing applications. The underlying continuous-time systems are transferred to the discrete-time domain using numerical integration methods. Within this contribution, effects on the qualitative behavior of the Andronov-Hopf bifurcation based systems concerning numerical integration methods are analyzed. It is shown exemplarily that explicit Runge-Kutta methods transform the truncated normalform equation of the Andronov-Hopf bifurcation into the normalform equation of the Neimark-Sacker bifurcation. Dependent on the order of the integration method, higher order terms are added during this transformation.A rescaled normalform equation of the Neimark-Sacker bifurcation is introduced that allows a parametric design of a discrete-time system which corresponds to the rescaled Andronov-Hopf system. This system approximates the characteristics of the rescaled Hopf-type amplifier for a large range of parameters. The natural frequency and the peak amplitude are preserved for every set of parameters. The Neimark-Sacker bifurcation based systems avoid large computational effort that would be caused by applying higher order integration methods to the continuous-time normalform equations.
EEG Analysis during complex diagnostic tasks in Nuclear Power Plants - Simulator-based Experimental Study
International Nuclear Information System (INIS)
Ha, Jun Su; Seong, Poong Hyun
In literature, there are a lot of studies based on EEG signals during cognitive activities of human-beings but most of them dealt with simple cognitive activities such as transforming letters into Morse code, subtraction, reading, semantic memory search, visual search, memorizing a set of words and so on. In this work, EEG signals were analyzed during complex diagnostic tasks in NPP simulator-based environment. Investigated are the theta, alpha, beta, and gamma band EEG powers during the diagnostic tasks. The experimental design and procedure are represented in section 2 and the results are shown in section 3. Finally some considerations are discussed and the direction for the further work is proposed in section 4
EEG Analysis during complex diagnostic tasks in Nuclear Power Plants - Simulator-based Experimental Study
Energy Technology Data Exchange (ETDEWEB)
Ha, Jun Su; Seong, Poong Hyun [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)
In literature, there are a lot of studies based on EEG signals during cognitive activities of human-beings but most of them dealt with simple cognitive activities such as transforming letters into Morse code, subtraction, reading, semantic memory search, visual search, memorizing a set of words and so on. In this work, EEG signals were analyzed during complex diagnostic tasks in NPP simulator-based environment. Investigated are the theta, alpha, beta, and gamma band EEG powers during the diagnostic tasks. The experimental design and procedure are represented in section 2 and the results are shown in section 3. Finally some considerations are discussed and the direction for the further work is proposed in section 4.
Solving the forward problem in EEG source analysis by spherical and fdm head modeling: a comparative analysis - biomed 2009
Vatta, F.; Meneghini, F.; Esposito, F.; Mininel, S.; Di Salle, F.
Neural source localization techniques based on electroencephalography (EEG) use scalp potential data to infer the location of underlying neural activity. This procedure entails modeling the sources of EEG activity and modeling the head volume conduction process to link the modeled sources to the
An introduction to nonlinear analysis and fixed point theory
Pathak, Hemant Kumar
This book systematically introduces the theory of nonlinear analysis, providing an overview of topics such as geometry of Banach spaces, differential calculus in Banach spaces, monotone operators, and fixed point theorems. It also discusses degree theory, nonlinear matrix equations, control theory, differential and integral equations, and inclusions. The book presents surjectivity theorems, variational inequalities, stochastic game theory and mathematical biology, along with a large number of applications of these theories in various other disciplines. Nonlinear analysis is characterised by its applications in numerous interdisciplinary fields, ranging from engineering to space science, hydromechanics to astrophysics, chemistry to biology, theoretical mechanics to biomechanics and economics to stochastic game theory. Organised into ten chapters, the book shows the elegance of the subject and its deep-rooted concepts and techniques, which provide the tools for developing more realistic and accurate models for ...
Investigation of attention deficit hyperactivity disorder (ADHD) sub-types in children via EEG frequency domain analysis.
Aldemir, Ramazan; Demirci, Esra; Per, Huseyin; Canpolat, Mehmet; Özmen, Sevgi; Tokmakçı, Mahmut
To investigate the frequency domain effects and changes in electroencephalography (EEG) signals in children diagnosed with attention deficit hyperactivity disorder (ADHD). The study contains 40 children. All children were between the ages of 7 and 12 years. Participants were classified into four groups which were ADHD (n=20), ADHD-I (ADHD-Inattentive type) (n=10), ADHD-C (ADHD-Combined type) (n=10), and control (n=20) groups. In this study, the frequency domain of EEG signals for ADHD, subtypes and control groups were analyzed and compared using Matlab software. The mean age of the ADHD children's group was 8.7 years and the control group 9.1 years. Spectral analysis of mean power (μV 2 ) and relative-mean power (%) was carried out for four different frequency bands: delta (0--4 Hz), theta (4--8 Hz), alpha (8--13 Hz) and beta (13--32 Hz). The ADHD and subtypes of ADHD-I, and ADHD-C groups had higher average power value of delta and theta band than that of control group. However, this is not the case for alpha and beta bands. Increases in delta/beta ratio and statistical significance were found only between ADHD-I and control group, and in delta/beta, theta/delta ratio statistical significance values were found to exist between ADHD-C and control group. EEG analyzes can be used as an alternative method when ADHD subgroups are identified.
Nonlinear dynamic analysis of hydrodynamically-coupled stainless steel structures
International Nuclear Information System (INIS)
Zhao, Y.
Spent nuclear fuel is usually stored temporarily on the site of nuclear power plants. The spent fuel storage racks are nuclear-safety-related stainless steel structures required to be analyzed for seismic loads. When the storage pool is subjected to three-dimensional (3-D) floor seismic excitations, rack modules, stored fuel bundles, adjacent racks and pool walls, and surrounding water are hydrodynamically coupled. Hydrodynamic coupling (HC) significantly affects the dynamic responses of the racks that are free-standing and submerged in water within the pool. A nonlinear time-history dynamic analysis is usually needed to describe the motion behavior of the racks that are both geometrically nonlinear and material nonlinear in nature. The nonlinearities include the friction resistance between the rack supporting legs and the pool floor, and various potential impacts of fuel-rack, rack-rack, and rack-pool wall. The HC induced should be included in the nonlinear dynamic analysis using the added-hydrodynamic-mass concept based on potential theory per the US Nuclear Regulatory Commission (USNRC) acceptance criteria. To this end, a finite element analysis constitutes a feasible and effective tool. However, most people perform somewhat simplified 1-D, or 2-D, or 3-D single rack and 2-D multiple rack analyses. These analyses are incomplete because a 3-D single rack model behaves quite differently from a 2-D mode. Furthermore, a 3-D whole pool multi-rack model behaves differently than a 3-D single rack model, especially when the strong HC effects are unsymmetrical. In this paper 3-D nonlinear dynamic time-history analyses were performed in a more quantitative manner using sophisticated finite element models developed for a single rack as well as all twelve racks in the whole-pool. Typical response results due to different HC effects are determined and discussed
Nonlinear control system analysis and design with Maple
Jager, de A.G.; Houstis, E.N.; Rice, J.R.
For the analysis and design of nonlinear control systems non-numerical methods are available. The required analytical computations are mostly too tedious to be done error free in a reasonable time by hand, so the use of symbolic computation programs can be of advantage. To show that the symbolic
Nonlinear analysis of shear deformable beam-columns partially ...
African Journals Online (AJOL)
In this paper, a boundary element method is developed for the nonlinear analysis of shear deformable beam-columns of arbitrary doubly symmetric simply or multiply connected constant cross section, partially supported on tensionless Winkler foundation, undergoing moderate large deflections under general boundary ...
Iterative analysis of concrete gravity dam-nonlinear foundation ...
African Journals Online (AJOL)
The solution of the coupled system is accomplished by solving the two systems separately and then considering the interaction effects at the soil–structure interface enforced by a developed iterative scheme. Emphasis has been laid on the study of material nonlinearity of the foundation material in the interaction analysis.
Linear and nonlinear stability analysis, associated to experimental fast reactors
International Nuclear Information System (INIS)
Amorim, E.S. do; Moura Neto, C. de; Rosa, M.A.P.
Phenomena associated to the physics of fast neutrons were analysed by linear and nonlinear Kinetics with arbitrary feedback. The theoretical foundations of linear kinetics and transfer functions aiming at the analysis of fast reactors stability, are established. These stability conditions were analitically proposed and investigated by digital and analogic programs. (E.G.) [pt
Nonlinear canonical correlation analysis with k sets of variables
van der Burg, Eeke; de Leeuw, Jan
The multivariate technique OVERALS is introduced as a non-linear generalization of canonical correlation analysis (CCA). First, two sets CCA is introduced. Two sets CCA is a technique that computes linear combinations of sets of variables that correlate in an optimal way. Two sets CCA is then
Experimental analysis of nonlinear problems in solid mechanics
International Nuclear Information System (INIS)
The booklet presents abstracts of papers from the Euromech Colloqium No. 152 held from Sept. 20th to 24th, 1982 in Wuppertal, Federal Republic of Germany. All the papers are dealing with Experimental Analysis of Nonlinear Problems in Solid Mechanics. (RW)
Practical Soil-Shallow Foundation Model for Nonlinear Structural Analysis
Directory of Open Access Journals (Sweden)
Moussa Leblouba
Full Text Available Soil-shallow foundation interaction models that are incorporated into most structural analysis programs generally lack accuracy and efficiency or neglect some aspects of foundation behavior. For instance, soil-shallow foundation systems have been observed to show both small and large loops under increasing amplitude load reversals. This paper presents a practical macroelement model for soil-shallow foundation system and its stability under simultaneous horizontal and vertical loads. The model comprises three spring elements: nonlinear horizontal, nonlinear rotational, and linear vertical springs. The proposed macroelement model was verified using experimental test results from large-scale model foundations subjected to small and large cyclic loading cases.
Nonlinear analysis of flexible plates lying on elastic foundation
Directory of Open Access Journals (Sweden)
Trushin Sergey
Full Text Available This article describes numerical procedures for analysis of flexible rectangular plates lying on elastic foundation. Computing models are based on the theory of plates with account of transverse shear deformations. The finite difference energy method of discretization is used for reducing the initial continuum problem to finite dimensional problem. Solution procedures for nonlinear problem are based on Newton-Raphson method. This theory of plates and numerical methods have been used for investigation of nonlinear behavior of flexible plates on elastic foundation with different properties.
Stability Analysis of Fractional-Order Nonlinear Systems with Delay
Directory of Open Access Journals (Sweden)
Yu Wang
Full Text Available Stability analysis of fractional-order nonlinear systems with delay is studied. We propose the definition of Mittag-Leffler stability of time-delay system and introduce the fractional Lyapunov direct method by using properties of Mittag-Leffler function and Laplace transform. Then some new sufficient conditions ensuring asymptotical stability of fractional-order nonlinear system with delay are proposed firstly. And the application of Riemann-Liouville fractional-order systems is extended by the fractional comparison principle and the Caputo fractional-order systems. Numerical simulations of an example demonstrate the universality and the effectiveness of the proposed method.
Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.
Lie, Octavian V; van Mierlo, Pieter
The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.
PyEEG: an open source Python module for EEG/MEG feature extraction.
Bao, Forrest Sheng; Liu, Xin; Zhang, Christina
Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.
Analysis of Nonlinear Dynamics by Square Matrix Method
Energy Technology Data Exchange (ETDEWEB)
Yu, Li Hua [Brookhaven National Lab. (BNL), Upton, NY (United States). Energy and Photon Sciences Directorate. National Synchrotron Light Source II
The nonlinear dynamics of a system with periodic structure can be analyzed using a square matrix. In this paper, we show that because the special property of the square matrix constructed for nonlinear dynamics, we can reduce the dimension of the matrix from the original large number for high order calculation to low dimension in the first step of the analysis. Then a stable Jordan decomposition is obtained with much lower dimension. The transformation to Jordan form provides an excellent action-angle approximation to the solution of the nonlinear dynamics, in good agreement with trajectories and tune obtained from tracking. And more importantly, the deviation from constancy of the new action-angle variable provides a measure of the stability of the phase space trajectories and their tunes. Thus the square matrix provides a novel method to optimize the nonlinear dynamic system. The method is illustrated by many examples of comparison between theory and numerical simulation. Finally, in particular, we show that the square matrix method can be used for optimization to reduce the nonlinearity of a system.
Brain wave correlates of attentional states: Event related potentials and quantitative EEG analysis during performance of cognitive and perceptual tasks
Freeman, Frederick G.
presented target stimulus. In addition to the task requirements, irrelevant tones were presented in the background. Research has shown that even though these stimuli are not attended, ERP's to them can still be elicited. The amplitude of the ERP waves has been shown to change as a function of a person's level of alertness. ERP's were also collected and analyzed for the target stimuli for each task. Brain maps were produced based on the ERP voltages for the different stimuli. In addition to the ERP's, a quantitative EEG (QEEG) was performed on the data using a fast Fourier technique to produce a power spectral analysis of the EEG. This analysis was conducted on the continuous EEG while the subjects were performing the tasks. Finally, a QEEG was performed on periods during the task when subjects indicated that they were in an altered state of awareness. During the tasks, subjects were asked to indicate by pressing a button when they realized their level of task awareness had changed. EEG epochs were collected for times just before and just after subjects made this reponse. The purpose of this final analysis was to determine whether or not subjective indices of level of awareness could be correlated with different patterns of EEG.
Homotopy analysis approach for nonlinear piezoelectric vibration energy harvesting
Directory of Open Access Journals (Sweden)
Shahlaei-Far Shahram
Full Text Available Piezoelectric energy harvesting from a vertical geometrically nonlinear cantilever beam with a tip mass subject to transverse harmonic base excitations is analyzed. One piezoelectric patch is placed on the slender beam to convert the tension and compression into electrical voltage. Applying the homotopy analysis method to the coupled electromechanical governing equations, we derive analytical solutions for the horizontal displacement of the tip mass and consequently the output voltage from the piezoelectric patch. Analytical approximation for the frequency response and phase of the geometrically forced nonlinear vibration system are also obtained. The research aims at a rigorous analytical perspective on a nonlinear problem which has previously been solely investigated by numerical and experimental methods.
Nonlinear analysis of reinforced concrete structures using software package abaqus
Directory of Open Access Journals (Sweden)
Marković Nemanja
Full Text Available Reinforced concrete (AB is characterized by huge inhomogeneity resulting from the material characteristics of the concrete, then, quasi-brittle behavior during failure. These and other phenomena require the introduction of material nonlinearity in the modeling of reinforced concrete structures. This paper presents the modeling reinforced concrete in the software package ABAQUS. A brief theoretical overview is presented of methods such as: Concrete Damage Plasticity (CDP, Smeared Concrete Cracking (CSC, Cap Plasticity (CP and Drucker-Prager model (DPM. We performed a nonlinear analysis of two-storey reinforced concrete frame by applying CDP method for modeling material nonlinearity of concrete. We have analyzed damage zones, crack propagation and loading-deflection ratio.
Methodology for global nonlinear analysis of nuclear systems
International Nuclear Information System (INIS)
Cacuci, D.G.; Cacuci, G.L.
This paper outlines a general method for globally computing the crucial features of nonlinear problems: bifurcations, limit points, saddle points, extrema (maxima and minima); our method also yields the local sensitivities (i.e., first order derivatives) of the system's state variables (e.g., fluxes, power, temperatures, flows) at any point in the system's phase space. We also present an application of this method to the nonlinear BWR model discussed in Refs. 8 and 11. The most significant novel feature of our method is the recasting of a general mathematical problem comprising three aspects: (1) nonlinear constrained optimization, (2) sensitivity analysis, into a fixed point problem of the form F[u(s), λ(s)] = 0 whose global zeros and singular points are related to the special features (i.e., extrema, bifurcations, etc.) of the original problem
Nonlinear systems techniques for dynamical analysis and control
Lefeber, Erjen; Arteaga, Ines
This treatment of modern topics related to the control of nonlinear systems is a collection of contributions celebrating the work of Professor Henk Nijmeijer and honoring his 60th birthday. It addresses several topics that have been the core of Professor Nijmeijer's work, namely: the control of nonlinear systems, geometric control theory, synchronization, coordinated control, convergent systems and the control of underactuated systems. The book presents recent advances in these areas, contributed by leading international researchers in systems and control. In addition to the theoretical questions treated in the text, particular attention is paid to a number of applications including (mobile) robotics, marine vehicles, neural dynamics and mechanical systems generally. This volume provides a broad picture of the analysis and control of nonlinear systems for scientists and engineers with an interest in the interdisciplinary field of systems and control theory. The reader will benefit from the expert participan...
Nonlinear Vibrations of Cantilever Timoshenko Beams: A Homotopy Analysis
Directory of Open Access Journals (Sweden)
Shahram Shahlaei-Far
Full Text Available Abstract This study analyzes the fourth-order nonlinear free vibration of a Timoshenko beam. We discretize the governing differential equation by Galerkin's procedure and then apply the homotopy analysis method (HAM to the obtained ordinary differential equation of the generalized coordinate. We derive novel analytical solutions for the nonlinear natural frequency and displacement to investigate the effects of rotary inertia, shear deformation, pre-tensile loads and slenderness ratios on the beam. In comparison to results achieved by perturbation techniques, this study demonstrates that a first-order approximation of HAM leads to highly accurate solutions, valid for a wide range of amplitude vibrations, of a high-order strongly nonlinear problem.
Nonlinear time series analysis with R
Huffaker, Ray; Rosa, Rodolfo
In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjec...
Analysis of nonlinear elastic behavior in miniature pneumatic artificial muscles
Hocking, Erica G.; Wereley, Norman M.
Pneumatic artificial muscles (PAMs) are well known for their excellent actuator characteristics, including high specific work, specific power, and power density. Recent research has focused on miniaturizing this pneumatic actuator technology in order to develop PAMs for use in small-scale mechanical systems, such as those found in robotic or aerospace applications. The first step in implementing these miniature PAMs was to design and characterize the actuator. To that end, this study presents the manufacturing process, experimental characterization, and analytical modeling of PAMs with millimeter-scale diameters. A fabrication method was developed to consistently produce low-cost, high performance, miniature PAMs using commercially available materials. The quasi-static behavior of these PAMs was determined through experimentation on a single actuator with an active length of 39.16 mm (1.54 in) and a diameter of 4.13 mm (0.1625 in). Testing revealed the PAM's full evolution of force with displacement for operating pressures ranging from 207 to 552 kPa (30-80 psi in 10 psi increments), as well as the blocked force and free contraction at each pressure. Three key nonlinear phenomena were observed: nonlinear PAM stiffness, hysteresis of the force versus displacement response for a given pressure, and a pressure deadband. To address the analysis of the nonlinear response of these miniature PAMs, a nonlinear stress versus strain model, a hysteresis model, and a pressure bias are introduced into a previously developed force balance analysis. Parameters of these nonlinear model refinements are identified from the measured force versus displacement data. This improved nonlinear force balance model is shown to capture the full actuation behavior of the miniature PAMs at each operating pressure and reconstruct miniature PAM response with much more accuracy than previously possible.
Analysis of nonlinear elastic behavior in miniature pneumatic artificial muscles
International Nuclear Information System (INIS)
Hocking, Erica G; Wereley, Norman M
Pneumatic artificial muscles (PAMs) are well known for their excellent actuator characteristics, including high specific work, specific power, and power density. Recent research has focused on miniaturizing this pneumatic actuator technology in order to develop PAMs for use in small-scale mechanical systems, such as those found in robotic or aerospace applications. The first step in implementing these miniature PAMs was to design and characterize the actuator. To that end, this study presents the manufacturing process, experimental characterization, and analytical modeling of PAMs with millimeter-scale diameters. A fabrication method was developed to consistently produce low-cost, high performance, miniature PAMs using commercially available materials. The quasi-static behavior of these PAMs was determined through experimentation on a single actuator with an active length of 39.16 mm (1.54 in) and a diameter of 4.13 mm (0.1625 in). Testing revealed the PAM's full evolution of force with displacement for operating pressures ranging from 207 to 552 kPa (30–80 psi in 10 psi increments), as well as the blocked force and free contraction at each pressure. Three key nonlinear phenomena were observed: nonlinear PAM stiffness, hysteresis of the force versus displacement response for a given pressure, and a pressure deadband. To address the analysis of the nonlinear response of these miniature PAMs, a nonlinear stress versus strain model, a hysteresis model, and a pressure bias are introduced into a previously developed force balance analysis. Parameters of these nonlinear model refinements are identified from the measured force versus displacement data. This improved nonlinear force balance model is shown to capture the full actuation behavior of the miniature PAMs at each operating pressure and reconstruct miniature PAM response with much more accuracy than previously possible. (paper)
Nonlinear Time Series Analysis via Neural Networks
Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin
This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.
Nonlinear structural analysis using integrated force method
Indian Academy of Sciences (India)
A new formulation termed the Integrated Force Method (IFM) was proposed by Patnaik ... nated ``Structure (nY m)'' where (nY m) are the force and displacement degrees of ..... Patnaik S N, Yadagiri S 1976 Frequency analysis of structures.
Applications of advances in nonlinear sensitivity analysis
Energy Technology Data Exchange (ETDEWEB)
Werbos, P J
The following paper summarizes the major properties and applications of a collection of algorithms involving differentiation and optimization at minimum cost. The areas of application include the sensitivity analysis of models, new work in statistical or econometric estimation, optimization, artificial intelligence and neuron modelling.
Finite element analysis of nonlinear creeping flows
International Nuclear Information System (INIS)
Loula, A.F.D.; Guerreiro, J.N.C.
Steady-state creep problems with monotone constitutive laws are studied. Finite element approximations are constructed based on mixed Petrov-Galerkin formulations for constrained problems. Stability, convergence and a priori error estimates are proved for equal-order discontinuous stress and continuous velocity interpolations. Numerical results are presented confirming the rates of convergence predicted in the analysis and the good performance of this formulation. (author) [pt
Investigation of True High Frequency Electrical Substrates of fMRI-Based Resting State Networks Using Parallel Independent Component Analysis of Simultaneous EEG/fMRI Data.
Kyathanahally, Sreenath P; Wang, Yun; Calhoun, Vince D; Deshpande, Gopikrishna
Previous work using simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data has shown that the slow temporal dynamics of resting state brain networks (RSNs), e.g., default mode network (DMN), visual network (VN), obtained from fMRI are correlated with smoothed and down sampled versions of various EEG features such as microstates and band-limited power envelopes. Therefore, even though the down sampled and smoothed envelope of EEG gamma band power is correlated with fMRI fluctuations in the RSNs, it does not mean that the electrical substrates of the RSNs fluctuate with periods state fMRI fluctuations in the RSNs, researchers have speculated that truly high frequency electrical substrates may exist for the RSNs, which would make resting fluctuations obtained from fMRI more meaningful to typically occurring fast neuronal processes in the sub-100 ms time scale. In this study, we test this critical hypothesis using an integrated framework involving simultaneous EEG/fMRI acquisition, fast fMRI sampling ( TR = 200 ms) using multiband EPI (MB EPI), and EEG/fMRI fusion using parallel independent component analysis (pICA) which does not require the down sampling of EEG to fMRI temporal resolution . Our results demonstrate that with faster sampling, high frequency electrical substrates (fluctuating with periods <100 ms time scale) of the RSNs can be observed. This provides a sounder neurophysiological basis for the RSNs.
Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal
Hamidreza Namazi; Amin Akrami; Sina Nazeri; Vladimir V. Kulish
An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally ...
Nonlinear dynamic analysis of 2-DOF nonlinear vibration isolation floating raft systems with feedback control
International Nuclear Information System (INIS)
Li Yingli; Xu Daolin; Fu Yiming; Zhou Jiaxi
In this paper, the average method is adopted to analysis dynamic characteristics of nonlinear vibration isolation floating raft system with feedback control. The analytic results show that the purposes of reducing amplitude of oscillation and complicating the motion can be achieved by adjusting properly the system parameters, exciting frequency and control gain. The conclusions can provide some available evidences for the design and improvement of both the passive and active control of the vibration isolation systems. By altering the exciting frequency and control gain, complex motion of the system can be obtained. Numerical simulations show the system exhibits period vibration, double period vibration and quasi-period motion.
Nonlinear dynamic analysis of nuclear reactor primary coolant systems
International Nuclear Information System (INIS)
Saffell, B.F. Jr.; Macek, R.W.; Thompson, T.R.; Lippert, R.F.
The ADINA computer code is utilized to perform mechanical response analysis of pressurized reactor primary coolant systems subjected to postulated loss-of-coolant accident (LOCA) loadings. Specifically, three plant analyses are performed utilizing the geometric and material nonlinear analysis capabilities of ADINA. Each reactor system finite element model represents the reactor vessel and internals, piping, major components, and component supports in a single coupled model. Material and geometric nonlinear capabilities of the beam and truss elements are employed in the formulation of each finite element model. Loadings applied to each plant for LOCA dynamic analysis include steady-state pressure, dead weight, strain energy release, transient piping hydraulic forces, and reactor vessel cavity pressurization. Representative results are presented with some suggestions for consideration in future ADINA code development
Probabilistic finite elements for transient analysis in nonlinear continua
Liu, W. K.; Belytschko, T.; Mani, A.
The probabilistic finite element method (PFEM), which is a combination of finite element methods and second-moment analysis, is formulated for linear and nonlinear continua with inhomogeneous random fields. Analogous to the discretization of the displacement field in finite element methods, the random field is also discretized. The formulation is simplified by transforming the correlated variables to a set of uncorrelated variables through an eigenvalue orthogonalization. Furthermore, it is shown that a reduced set of the uncorrelated variables is sufficient for the second-moment analysis. Based on the linear formulation of the PFEM, the method is then extended to transient analysis in nonlinear continua. The accuracy and efficiency of the method is demonstrated by application to a one-dimensional, elastic/plastic wave propagation problem. The moments calculated compare favorably with those obtained by Monte Carlo simulation. Also, the procedure is amenable to implementation in deterministic FEM based computer programs.
Non-linear analysis in Light Water Reactor design
International Nuclear Information System (INIS)
Rashid, Y.R.; Sharabi, M.N.; Nickell, R.E.; Esztergar, E.P.; Jones, J.W.
The results obtained from a scoping study sponsored by the US Department of Energy (DOE) under the Light Water Reactor (LWR) Safety Technology Program at Sandia National Laboratories are presented. Basically, this project calls for the examination of the hypothesis that the use of nonlinear analysis methods in the design of LWR systems and components of interest include such items as: the reactor vessel, vessel internals, nozzles and penetrations, component support structures, and containment structures. Piping systems are excluded because they are being addressed by a separate study. Essentially, the findings were that nonlinear analysis methods are beneficial to LWR design from a technical point of view. However, the costs needed to implement these methods are the roadblock to readily adopting them. In this sense, a cost-benefit type of analysis must be made on the various topics identified by these studies and priorities must be established. This document is the complete report by ANATECH International Corporation
A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects.
Doborjeh, Maryam Gholami; Wang, Grace Y; Kasabov, Nikola K; Kydd, Robert; Russell, Bruce
This paper introduces a method utilizing spiking neural networks (SNN) for learning, classification, and comparative analysis of brain data. As a case study, the method was applied to electroencephalography (EEG) data collected during a GO/NOGO cognitive task performed by untreated opiate addicts, those undergoing methadone maintenance treatment (MMT) for opiate dependence and a healthy control group. the method is based on an SNN architecture called NeuCube, trained on spatiotemporal EEG data. NeuCube was used to classify EEG data across subject groups and across GO versus NOGO trials, but also facilitated a deeper comparative analysis of the dynamic brain processes. This analysis results in a better understanding of human brain functioning across subject groups when performing a cognitive task. In terms of the EEG data classification, a NeuCube model obtained better results (the maximum obtained accuracy: 90.91%) when compared with traditional statistical and artificial intelligence methods (the maximum obtained accuracy: 50.55%). more importantly, new information about the effects of MMT on cognitive brain functions is revealed through the analysis of the SNN model connectivity and its dynamics. this paper presented a new method for EEG data modeling and revealed new knowledge on brain functions associated with mental activity which is different from the brain activity observed in a resting state of the same subjects.
Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests.
Anastasiadou, Maria N; Christodoulakis, Manolis; Papathanasiou, Eleftherios S; Papacostas, Savvas S; Mitsis, Georgios D
This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert
EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.
Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang
Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.
The Importance of Nonlinear Transformations Use in Medical Data Analysis.
Shachar, Netta; Mitelpunkt, Alexis; Kozlovski, Tal; Galili, Tal; Frostig, Tzviel; Brill, Barak; Marcus-Kalish, Mira; Benjamini, Yoav
The accumulation of data and its accessibility through easier-to-use platforms will allow data scientists and practitioners who are less sophisticated data analysts to get answers by using big data for many purposes in multiple ways. Data scientists working with medical data are aware of the importance of preprocessing, yet in many cases, the potential benefits of using nonlinear transformations is overlooked. Our aim is to present a semi-automated approach of symmetry-aiming transformations tailored for medical data analysis and its advantages. We describe 10 commonly encountered data types used in the medical field and the relevant transformations for each data type. Data from the Alzheimer's Disease Neuroimaging Initiative study, Parkinson's disease hospital cohort, and disease-simulating data were used to demonstrate the approach and its benefits. Symmetry-targeted monotone transformations were applied, and the advantages gained in variance, stability, linearity, and clustering are demonstrated. An open source application implementing the described methods was developed. Both linearity of relationships and increase of stability of variability improved after applying proper nonlinear transformation. Clustering simulated nonsymmetric data gave low agreement to the generating clusters (Rand value=0.681), while capturing the original structure after applying nonlinear transformation to symmetry (Rand value=0.986). This work presents the use of nonlinear transformations for medical data and the importance of their semi-automated choice. Using the described approach, the data analyst increases the ability to create simpler, more robust and translational models, thereby facilitating the interpretation and implementation of the analysis by medical practitioners. Applying nonlinear transformations as part of the preprocessing is essential to the quality and interpretability of results. ©Netta Shachar, Alexis Mitelpunkt, Tal Kozlovski, Tal Galili, Tzviel Frostig, Barak
Slope stability analysis using limit equilibrium method in nonlinear criterion.
Lin, Hang; Zhong, Wenwen; Xiong, Wei; Tang, Wenyu
In slope stability analysis, the limit equilibrium method is usually used to calculate the safety factor of slope based on Mohr-Coulomb criterion. However, Mohr-Coulomb criterion is restricted to the description of rock mass. To overcome its shortcomings, this paper combined Hoek-Brown criterion and limit equilibrium method and proposed an equation for calculating the safety factor of slope with limit equilibrium method in Hoek-Brown criterion through equivalent cohesive strength and the friction angle. Moreover, this paper investigates the impact of Hoek-Brown parameters on the safety factor of slope, which reveals that there is linear relation between equivalent cohesive strength and weakening factor D. However, there are nonlinear relations between equivalent cohesive strength and Geological Strength Index (GSI), the uniaxial compressive strength of intact rock σ ci , and the parameter of intact rock m i . There is nonlinear relation between the friction angle and all Hoek-Brown parameters. With the increase of D, the safety factor of slope F decreases linearly; with the increase of GSI, F increases nonlinearly; when σ ci is relatively small, the relation between F and σ ci is nonlinear, but when σ ci is relatively large, the relation is linear; with the increase of m i , F decreases first and then increases.
Nonlinear analysis of piezoelectric nanocomposite energy harvesting plates
International Nuclear Information System (INIS)
Rafiee, M; He, X Q; Liew, K M
This paper investigates the nonlinear analysis of energy harvesting from piezoelectric functionally graded carbon nanotube reinforced composite plates under combined thermal and mechanical loadings. The excitation, which derives from harmonically varying mechanical in-plane loading, results in parametric excitation. The governing equations of the piezoelectric functionally graded carbon nanotube reinforced composite plates are derived based on classical plate theory and von Kármán geometric nonlinearity. The material properties of the nanocomposite plate are assumed to be graded in the thickness direction. The single-walled carbon nanotubes (SWCNTs) are assumed to be aligned, straight and have a uniform layout. The linear buckling and vibration behavior of the nanocomposite plates is obtained in the first step. Then, Galerkin's method is employed to derive the nonlinear governing equations of the problem with cubic nonlinearities associated with mid-plane stretching. Periodic solutions are determined by using the Poincaré–Lindstedt perturbation scheme with movable simply supported boundary conditions. The effects of temperature change, the volume fraction and the distribution pattern of the SWCNTs on the parametric resonance, in particular the amplitude of vibration and the average harvested power of the smart functionally graded carbon nanotube reinforced composite plates, are investigated through a detailed parametric study. (paper)
Nonlinear Finite Element Analysis of Reinforced Concrete Shells
Directory of Open Access Journals (Sweden)
Mustafa K. Ahmed
Full Text Available This investigation is to develop a numerical model suitable for nonlinear analysis of reinforced concrete shells. A nine-node Lagrangian element Figure (1 with enhanced shear interpolation will be used in this study. Table (1 describes shape functions and their derivatives of this element.An assumed transverse shear strain is used in the formulation of this element to overcome shear locking. Degenerated quadratic thick plate elements employing a layered discrelization through the thickness will be adopted. Different numbers of layers for different thickness can be used per element. A number of layers between (6 and 10 have proved to be appropriate to represent the nonlinear material behavior in structures. In this research 8 layers will be adequate. Material nonlinearities due to cracking of concrete, plastic flow or crushing of concrete in compression and yield condition of reinforcing steel are considered. The maximum tensile strength is used as a criterion for crack initiation. Attention is given to the tension stiffening phenomenon and the degrading effect of cracking on the compressive and shear strength of concrete. Perfect bond between concrete and steel is assumed. Attention is given also to geometric nonlinearities. An example have been chosen in order to demonstrate the suitability of the models by comparing the predicted behaviour with the experimental results for shell exhibiting various modes of failure.
Nonlinear optical polarization analysis in chemistry and biology
Simpson, Garth J
This rigorous yet accessible guide presents a molecular-based description of nonlinear optical polarization analysis of chemical and biological assemblies. It includes discussion of the most common nonlinear optical microscopy and interfacial measurements used for quantitative analysis, specifically second harmonic generation (SHG), two-photon excited fluorescence (2PEF), vibrational sum frequency generation (SFG), and coherent anti-Stokes Raman spectroscopy/stimulated Raman spectroscopy (CARS/SRS). A linear algebra mathematical framework is developed, allowing step-wise systematic connections to be made between the observable measurements and the molecular response. Effects considered include local field corrections, the molecular orientation distribution, rotations between the molecular frame, the local frame and the laboratory frame, and simplifications from molecular and macromolecular symmetry. Specific examples are provided throughout the book, working from the common and relatively simple case studies ...
Non-linear seismic analysis of structures coupled with fluid
International Nuclear Information System (INIS)
Descleve, P.; Derom, P.; Dubois, J.
This paper presents a method to calculate non-linear structure behaviour under horizontal and vertical seismic excitation, making possible the full non-linear seismic analysis of a reactor vessel. A pseudo forces method is used to introduce non linear effects and the problem is solved by superposition. Two steps are used in the method: - Linear calculation of the complete model. - Non linear analysis of thin shell elements and calculation of seismic induced pressure originating from linear and non linear effects, including permanent loads and thermal stresses. Basic aspects of the mathematical formulation are developed. It has been applied to axi-symmetric shell element using a Fourier series solution. For the fluid interaction effect, a comparison is made with a dynamic test. In an example of application, the displacement and pressure time history are given. (orig./GL)
Phase space analysis for anisotropic universe with nonlinear bulk viscosity
Sharif, M.; Mumtaz, Saadia
In this paper, we discuss phase space analysis of locally rotationally symmetric Bianchi type I universe model by taking a noninteracting mixture of dust like and viscous radiation like fluid whose viscous pressure satisfies a nonlinear version of the Israel-Stewart transport equation. An autonomous system of equations is established by defining normalized dimensionless variables. In order to investigate stability of the system, we evaluate corresponding critical points for different values of the parameters. We also compute power-law scale factor whose behavior indicates different phases of the universe model. It is found that our analysis does not provide a complete immune from fine-tuning because the exponentially expanding solution occurs only for a particular range of parameters. We conclude that stable solutions exist in the presence of nonlinear model for bulk viscosity with different choices of the constant parameter m for anisotropic universe.
Like/dislike analysis using EEG: determination of most discriminative channels and frequencies.
Yılmaz, Bülent; Korkmaz, Sümeyye; Arslan, Dilek Betül; Güngör, Evrim; Asyalı, Musa H
In this study, we have analyzed electroencephalography (EEG) signals to investigate the following issues, (i) which frequencies and EEG channels could be relatively better indicators of preference (like or dislike decisions) of consumer products, (ii) timing characteristic of "like" decisions during such mental processes. For this purpose, we have obtained multichannel EEG recordings from 15 subjects, during total of 16 epochs of 10 s long, while they were presented with some shoe photographs. When they liked a specific shoe, they pressed on a button and marked the time of this activity and the particular epoch was labeled as a LIKE case. No button press meant that the subject did not like the particular shoe that was displayed and corresponding epoch designated as a DISLIKE case. After preprocessing, power spectral density (PSD) of EEG data was estimated at different frequencies (4, 5, …, 40 Hz) using the Burg method, for each epoch corresponding to one shoe presentation. Each subject's data consisted of normalized PSD values (NPVs) from all LIKE and DISLIKE cases/epochs coming from all 19 EEG channels. In order to determine the most discriminative frequencies and channels, we have utilized logistic regression, where LIKE/DISLIKE status was used as a categorical (binary) response variable and corresponding NPVs were the continuously valued input variables or predictors. We observed that when all the NPVs (total of 37) are used as predictors, the regression problem was becoming ill-posed due to large number of predictors (compared to the number of samples) and high correlation among predictors. To circumvent this issue, we have divided the frequency band into low frequency (LF) 4-19 Hz and high frequency (HF) 20-40 Hz bands and analyzed the influence of the NPV in these bands separately. Then, using the p-values that indicate how significantly estimated predictor weights are different than zero, we have determined the NPVs and channels that are more influential
Linear Algebraic Method for Non-Linear Map Analysis
International Nuclear Information System (INIS)
Yu, L.; Nash, B.
We present a newly developed method to analyze some non-linear dynamics problems such as the Henon map using a matrix analysis method from linear algebra. Choosing the Henon map as an example, we analyze the spectral structure, the tune-amplitude dependence, the variation of tune and amplitude during the particle motion, etc., using the method of Jordan decomposition which is widely used in conventional linear algebra.
Elements of nonlinear time series analysis and forecasting
De Gooijer, Jan G
This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a "theorem-proof" format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible...
Transcranial direct current stimulation generates a transient increase of small-world in brain connectivity: an EEG graph theoretical analysis.
Vecchio, Fabrizio; Di Iorio, Riccardo; Miraglia, Francesca; Granata, Giuseppe; Romanello, Roberto; Bramanti, Placido; Rossini, Paolo Maria
Transcranial direct current stimulation (tDCS) is a non-invasive technique able to modulate cortical excitability in a polarity-dependent way. At present, only few studies investigated the effects of tDCS on the modulation of functional connectivity between remote cortical areas. The aim of this study was to investigate-through graph theory analysis-how bipolar tDCS modulate cortical networks high-density EEG recordings were acquired before and after bipolar cathodal, anodal and sham tDCS involving the primary motor and pre-motor cortices of the dominant hemispherein 14 healthy subjects. Results showed that, after bipolar anodal tDCS stimulation, brain networks presented a less evident "small world" organization with a global tendency to be more random in its functional connections with respect to prestimulus condition in both hemispheres. Results suggest that tDCS globally modulates the cortical connectivity of the brain, modifying the underlying functional organization of the stimulated networks, which might be related to changes in synaptic efficiency of the motor network and related brain areas. This study demonstrated that graph analysis approach to EEG recordings is able to intercept changes in cortical functions mediated by bipolar anodal tDCS mainly involving the dominant M1 and related motor areas. Concluding, tDCS could be an useful technique to help understanding brain rhythms and their topographic functional organization and specificity.
Complexity of EEG-signal in Time Domain - Possible Biomedical Application
Klonowski, Wlodzimierz; Olejarczyk, Elzbieta; Stepien, Robert
Human brain is a highly complex nonlinear system. So it is not surprising that in analysis of EEG-signal, which represents overall activity of the brain, the methods of Nonlinear Dynamics (or Chaos Theory as it is commonly called) can be used. Even if the signal is not chaotic these methods are a motivating tool to explore changes in brain activity due to different functional activation states, e.g. different sleep stages, or to applied therapy, e.g. exposure to chemical agents (drugs) and physical factors (light, magnetic field). The methods supplied by Nonlinear Dynamics reveal signal characteristics that are not revealed by linear methods like FFT. Better understanding of principles that govern dynamics and complexity of EEG-signal can help to find `the signatures' of different physiological and pathological states of human brain, quantitative characteristics that may find applications in medical diagnostics.
Nonlinear analysis of end slabs in prestressed concrete reactor vessels
International Nuclear Information System (INIS)
Abdulrahman, H.O.
A procedure for the nonlinear analysis of end slabs is prestressed concrete reactor vessels (PCRVs), based on the finite element method, is presented. The applicability of the procedure to the ultimate load analysis of small-scale models of the primary containment of nuclear reactors is shown. Material nonlinearity only is considered. The procedure utilizes the four-node linear quadrilateral isoparametric element with the choice of incorporating the nonconforming modes. This element is used for modeling the vessel as an axisymmetric solid. Concrete is assumed to be an isotropic material in the elastic range. The compressive stresses are judged according to a special form of the Mohr-Coulomb criterion. The nonlinear problem was solved using a generalized Newton-Raphson procedure. A detailed example problem of a pressure vessel with penetrations is presented. This is followed by a summary of the other cases studied. The solutions obtained match very closely the measured response of the test vessels under increasing internal pressure up to failure. The procedure is thus adequate for the assessment of the ultimate load behavior and failure of actual pressure vessels with a moderate demand on human and computational resources
Nonlinear modal analysis in NPP dynamics: a proposal
International Nuclear Information System (INIS)
Suarez Antola, R.
We propose and briefly suggest how to apply the analytical tools of nonlinear modal analysis (NMA) to problems of nuclear reactor kinetics, NPP dynamics, and NPP instrumentation and control. The proposed method is closely related with recent approaches by modal analysis using the reactivity matrix with feedbacks to couple neutron kinetics with thermal hydraulics in the reactors core. A nonlinear system of ordinary differential equations for mode amplitudes is obtained, projecting the dynamic equations of a model of NPP onto the eigenfunctions of a suitable adjoint operator. A steady state solution of the equations is taken as a reference, and the behaviour of transient solutions in some neighbourhood of the steady state solution is studied by an extension of Liapunov's First Method that enables to cope directly with the non-linear terms in the dynamics. In NPP dynamics these differential equations for the mode amplitudes are of polynomial type of low degree A few dominant modes can usually be identified. These mode amplitudes evolve almost independently of the other modes, more slowly and tending to slave the other mode amplitudes. Using asymptotic methods, it is possible to calculate a closed form analytical approximation to the response to finite amplitude perturbations from the given steady spatial pattern (the origin of the space of mode amplitudes).When there is finite amplitude instability, the method allows us to calculate the threshold amplitude as a well defined function of system's parameters. This is a most significant accomplishment that the other methods cannot afford
Non-linear elastic thermal stress analysis with phase changes
International Nuclear Information System (INIS)
Amada, S.; Yang, W.H.
The non-linear elastic, thermal stress analysis with temperature induced phase changes in the materials is presented. An infinite plate (or body) with a circular hole (or tunnel) is subjected to a thermal loading on its inner surface. The peak temperature around the hole reaches beyond the melting point of the material. The non-linear diffusion equation is solved numerically using the finite difference method. The material properties change rapidly at temperatures where the change of crystal structures and solid-liquid transition occur. The elastic stresses induced by the transient non-homogeneous temperature distribution are calculated. The stresses change remarkably when the phase changes occur and there are residual stresses remaining in the plate after one cycle of thermal loading. (Auth.)
Nonlinear dynamic analysis using Petrov-Galerkin natural element method
International Nuclear Information System (INIS)
Lee, Hong Woo; Cho, Jin Rae
According to our previous study, it is confirmed that the Petrov-Galerkin Natural Element Method (PG-NEM) completely resolves the numerical integration inaccuracy in the conventional Bubnov-Galerkin Natural Element Method (BG-NEM). This paper is an extension of PG-NEM to two-dimensional nonlinear dynamic problem. For the analysis, a constant average acceleration method and a linearized total Lagrangian formulation is introduced with the PG-NEM. At every time step, the grid points are updated and the shape functions are reproduced from the relocated nodal distribution. This process enables the PG-NEM to provide more accurate and robust approximations. The representative numerical experiments performed by the test Fortran program, and the numerical results confirmed that the PG-NEM effectively and accurately approximates the nonlinear dynamic problem
A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings
Directory of Open Access Journals (Sweden)
Logothetis Nikos K
Full Text Available Abstract Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Results Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. Conclusion The new toolbox presented here implements fast
A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings.
Magri, Cesare; Whittingstall, Kevin; Singh, Vanessa; Logothetis, Nikos K; Panzeri, Stefano
Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. The new toolbox presented here implements fast and data-robust computations of the most relevant
Advances in nonlinear vibration analysis of structures. Part-I. Beams
Indian Academy of Sciences (India)
element analysis of nonlinear beams under static and dynamic loads. ... linearization, substitution of inplane boundary conditions at element level rather .... Modelling the nonlinear vibration problems using finite elements, albeit with a couple.
Nonlinear Aerodynamics-Structure Time Simulation for HALE Aircraft Design/Analysis, Phase I
National Aeronautics and Space Administration — Time simulation of a nonlinear aerodynamics model (NA) developed at Virginia Tech coupled with a nonlinear structure model (NS) is proposed as a design/analysis...
EEG resting state functional connectivity analysis in children with benign epilepsy with centrotemporal spikes
Directory of Open Access Journals (Sweden)
Azeez eAdebimpe
Full Text Available In this study, we investigated changes in functional connectivity of the brain networks in patients with benign epilepsy with centrotemporal spikes compared to healthy controls using high-density EEG data collected under eyes-closed resting state condition. EEG source reconstruction was performed with exact Low Resolution Electromagnetic Tomography (eLORETA. We investigated functional connectivity (FC between 84 Brodmann areas using lagged phase synchronization (LPS in four frequency bands (δ, θ, α, and β. We further computed the network degree, clustering coefficient and efficiency. Compared to controls, patients displayed higher θ and α and lower β lagged phase synchronization values. In these frequency bands, patients were also characterized by less well ordered brain networks exhibiting higher global degrees and efficiencies and lower clustering coefficients. In the beta band, patients exhibited reduced functional segregation and integration due to loss of both local and long-distance functional connections. These findings suggest that benign epileptic brain networks might be functionally disrupted due to their altered functional organization especially in the α and β frequency bands.
EEG Spectral Analysis in Serious Gaming: An Ad Hoc Experimental Application
Directory of Open Access Journals (Sweden)
Minchev Z.
Full Text Available The application of serious gaming technology in different areas of human knowledge for learning is raising the question of quantitative measurement of the training process quality. In the present paper a pilot study of 10 healthy volunteers' EEG spectra is performed for ad hoc selected game events ('win' and 'lose' via continuous wavelet transform (real and complex on the basis of the Morlet mother wavelet function and S-transformation. The results have shown a general decrease of the alpha rhythms power spectra frequencies for the 'lose' events and increase for the 'win' events. This fact corresponds to an opposite behaviour of the theta rhythm of the players for the same 'win' and 'lose' events. Additionally, the frequency changes in the alpha1 (8-10.5 Hz, alpha2 (10.5-13 Hz and theta2 rhythms (6-8 Hz were supposed to be a phenomena related to positive and negative emotions appearance in the EEG activity of the players regarding the selected 'win' and 'lose' states.
Analysis of EEG activity in response to binaural beats with different frequencies.
Gao, Xiang; Cao, Hongbao; Ming, Dong; Qi, Hongzhi; Wang, Xuemin; Wang, Xiaolu; Chen, Runge; Zhou, Peng
When two coherent sounds with nearly similar frequencies are presented to each ear respectively with stereo headphones, the brain integrates the two signals and produces a sensation of a third sound called binaural beat (BB). Although earlier studies showed that BB could influence behavior and cognition, common agreement on the mechanism of BB has not been reached yet. In this work, we employed Relative Power (RP), Phase Locking Value (PLV) and Cross-Mutual Information (CMI) to track EEG changes during BB stimulations. EEG signals were acquired from 13 healthy subjects. Five-minute BBs with four different frequencies were tested: delta band (1 Hz), theta band (5 Hz), alpha band (10 Hz) and beta band (20 Hz). We observed RP increase in theta and alpha bands and decrease in beta band during delta and alpha BB stimulations. RP decreased in beta band during theta BB, while RP decreased in theta band during beta BB. However, no clear brainwave entrainment effect was identified. Connectivity changes were detected following the variation of RP during BB stimulations. Our observation supports the hypothesis that BBs could affect functional brain connectivity, suggesting that the mechanism of BB-brain interaction is worth further study. Copyright © 2014. Published by Elsevier B.V.
Develop advanced nonlinear signal analysis topographical mapping system
The Space Shuttle Main Engine (SSME) has been undergoing extensive flight certification and developmental testing, which involves some 250 health monitoring measurements. Under the severe temperature, pressure, and dynamic environments sustained during operation, numerous major component failures have occurred, resulting in extensive engine hardware damage and scheduling losses. To enhance SSME safety and reliability, detailed analysis and evaluation of the measurements signal are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce catastrophic system failure risks and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. The basic objective of this contract are threefold: (1) develop and validate a hierarchy of innovative signal analysis techniques for nonlinear and nonstationary time-frequency analysis. Performance evaluation will be carried out through detailed analysis of extensive SSME static firing and flight data. These techniques will be incorporated into a fully automated system; (2) develop an advanced nonlinear signal analysis topographical mapping system (ATMS) to generate a Compressed SSME TOPO Data Base (CSTDB). This ATMS system will convert tremendous amount of complex vibration signals from the entire SSME test history into a bank of succinct image-like patterns while retaining all respective phase information. High compression ratio can be achieved to allow minimal storage requirement, while providing fast signature retrieval, pattern comparison, and identification capabilities; and (3) integrate the nonlinear correlation techniques into the CSTDB data base with compatible TOPO input data format. Such integrated ATMS system will provide the large test archives necessary for quick signature comparison. This study will provide timely assessment of SSME component operational status, identify probable causes of
Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal.
Namazi, Hamidreza; Akrami, Amin; Nazeri, Sina; Kulish, Vladimir V
An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose.
A differentiating empirical linguistic analysis of dreamer activity in reports of EEG-controlled REM-dreams and hypnagogic hallucinations.
Speth, Jana; Frenzel, Clemens; Voss, Ursula
We present Activity Analysis as a new method for the quantification of subjective reports of altered states of consciousness with regard to the indicated level of simulated motor activity. Empirical linguistic activity analysis was conducted with dream reports conceived immediately after EEG-controlled periods of hypnagogic hallucinations and REM-sleep in the sleep laboratory. Reports of REM-dreams exhibited a significantly higher level of simulated physical dreamer activity, while hypnagogic hallucinations appear to be experienced mostly from the point of passive observer. This study lays the groundwork for clinical research on the level of simulated activity in pathologically altered states of subjective experience, for example in the REM-dreams of clinically depressed patients, or in intrusions and dreams of patients diagnosed with PTSD. Copyright © 2013 Elsevier Inc. All rights reserved.
Lower bound limit analysis of slabs with nonlinear yield criteria
DEFF Research Database (Denmark)
Krabbenhøft, Kristian; Damkilde, Lars
A finite element formulation of the limit analysis of perfectly plastic slabs is given. An element with linear moment fields for which equilibrium is satisfied exactly is used in connection with an optimization algorithm taking into account the full nonlinearity of the yield criteria. Both load...... and material optimization problems are formulated and by means of the duality theory of linear programming the displacements are extracted from the dual variables. Numerical examples demonstrating the capabilities of the method and the effects of using a more refined representation of the yield criteria...
Nonlinear analysis of shock absorbers with amplitude-dependent damping
Łuczko, Jan; Ferdek, Urszula; Łatas, Waldemar
This paper contains an analysis of a quarter-car model representing a vehicle equipped with a hydraulic damper whose characteristics are dependent on the piston stroke. The damper, compared to a classical mono-tube damper, has additional internal chambers. Oil flow in those chambers is controlled by relative piston displacement. The proposed nonlinear model of the system is aimed to test the effect of key design parameters of the damper on the quality indices representing ride comfort and driving safety. Numerical methods were used to determine the characteristic curves of the damper and responses of the system to harmonic excitations with their amplitude decreasing as the values of frequency increase.
Stability Analysis of Some Nonlinear Anaerobic Digestion Models
Directory of Open Access Journals (Sweden)
Ivan Simeonov
Full Text Available Abstract: The paper deals with local asymptotic stability analysis of some mass balance dynamic models (based on one and on two-stage reaction schemes of the anaerobic digestion (AD in CSTR. The equilibrium states for models based on one (with Monod, Contois and Haldane shapes for the specific growth rate and on two-stage (only with Monod shapes for both the specific growth rate of acidogenic and methanogenic bacterial populations reaction schemes have been determined solving sets of nonlinear algebraic equations using Maples. Their stability has been analyzed systematically, which provides insight and guidance for AD bioreactors design, operation and control.
Nonlinear Principal Component Analysis Using Strong Tracking Filter
Institute of Scientific and Technical Information of China (English)
The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.
[The Influence of the Functional State of Brain Regulatory Structures on the Programming, Selective Regulation and Control of Cognitive Activity in Children. Report I: Neuropsychological and EEG Analysis of Age-Related Changes in Brain Regulatory Functions in Children Aged 9-12 Years].
Semenova, A; Machinskaya, R I; Lomakin, D I
Age-related changes in brain regulatory functions in children aged from 9 to 12 years with typical development were studied by means of neuropsychological and EEG analysis. The participants of the study were 107 children without learning difficulties and behavior deviations; they were devided into three groups (9-10, 10-11 and 11-12 years). The neuropsychological tests revealed nonlinear age-related changes in different executive brain functions. The group of 10-11-year-old children showed better results in programming, in- hibition of impulsive reactions and in the perception of socially relevant information than the group of 9-10- year-old children. At the same time, these children had more difficulties with selective activity regulation as compared with the younger group. The difficulties were mainly caused by switching from one element of the program to another and by retention of learned sequence of actions. These children also showed a lower level of motivation for task performance. The children aged 11-12 years had less difficulties with selective activity regulation; however, impulsive behavior was more frequent; these children also had a higher level of task performance motivation than in children aged 10-11 years. The analysis of resting state EEG revealed age-related differences in deviated EEG patterns associated with non-optimal functioning of fronto-thalamic system and hypothalamic structures. The incidence of these two types of EEG patterns was significantly higher in children aged 10-11 years as compared with children aged 9-10 years. The EEG of the groups of 10-11 and 11-12-years-old children did not show any significant differences.
a Signal-Tuned Gabor Transform with Application to Eeg Analysis
Torreão, José R. A.; Victer, Silvia M. C.; Fernandes, João L.
We introduce a time-frequency transform based on Gabor functions whose parameters are given by the Fourier transform of the analyzed signal. At any given frequency, the width and the phase of the Gabor function are obtained, respectively, from the magnitude and the phase of the signal's corresponding Fourier component, yielding an analyzing kernel which is a representation of the signal's content at that particular frequency. The resulting Gabor transform tunes itself to the input signal, allowing the accurate detection of time and frequency events, even in situations where the traditional Gabor and S-transform approaches tend to fail. This is the case, for instance, when considering the time-frequency representation of electroencephalogram traces (EEG) of epileptic subjects, as illustrated by the experimental study presented here.
Parameterized entropy analysis of EEG following hypoxic-ischemic brain injury
International Nuclear Information System (INIS)
Tong Shanbao; Bezerianos, Anastasios; Malhotra, Amit; Zhu Yisheng; Thakor, Nitish
In the present study Tsallis and Renyi entropy methods were used to study the electric activity of brain following hypoxic-ischemic (HI) injury. We investigated the performances of these parameterized information measures in describing the electroencephalogram (EEG) signal of controlled experimental animal HI injury. The results show that (a): compared with Shannon and Renyi entropy, the parameterized Tsallis entropy acts like a spatial filter and the information rate can either tune to long range rhythms or to short abrupt changes, such as bursts or spikes during the beginning of recovery, by the entropic index q; (b): Renyi entropy is a compact and predictive indicator for monitoring the physiological changes during the recovery of brain injury. There is a reduction in the Renyi entropy after brain injury followed by a gradual recovery upon resuscitation
EEG Analysis of the Brain Activity during the Observation of Commercial, Political, or Public Service Announcements
Directory of Open Access Journals (Sweden)
Giovanni Vecchiato
Full Text Available The use of modern brain imaging techniques could be useful to understand what brain areas are involved in the observation of video clips related to commercial advertising, as well as for the support of political campaigns, and also the areas of Public Service Announcements (PSAs. In this paper we describe the capability of tracking brain activity during the observation of commercials, political spots, and PSAs with advanced high-resolution EEG statistical techniques in time and frequency domains in a group of normal subjects. We analyzed the statistically significant cortical spectral power activity in different frequency bands during the observation of a commercial video clip related to the use of a beer in a group of 13 normal subjects. In addition, a TV speech of the Prime Minister of Italy was analyzed in two groups of swing and "supporter" voters. Results suggested that the cortical activity during the observation of commercial spots could vary consistently across the spot. This fact suggest the possibility to remove the parts of the spot that are not particularly attractive by using those cerebral indexes. The cortical activity during the observation of the political speech indicated a major cortical activity in the supporters group when compared to the swing voters. In this case, it is possible to conclude that the communication proposed has failed to raise attention or interest on swing voters. In conclusions, high-resolution EEG statistical techniques have been proved to able to generate useful insights about the particular fruition of TV messages, related to both commercial as well as political fields.
EEG based time and frequency dynamics analysis of visually induced motion sickness (VIMS).
Arsalan Naqvi, Syed Ali; Badruddin, Nasreen; Jatoi, Munsif Ali; Malik, Aamir Saeed; Hazabbah, Wan; Abdullah, Baharudin
3D movies are attracting the viewers as they can see the objects flying out of the screen. However, many viewers have reported various problems which are usually faced after watching 3D movies. These problems include visual fatigue, eye strain, headaches, dizziness, blurred vision or collectively may be termed as visually induced motion sickness (VIMS). This research focuses on the comparison between 3D passive technology with a conventional 2D technology to find that whether 3D is causing trouble in the viewers or not. For this purpose, an experiment was designed in which participants were randomly assigned to watch 2D or a 3D movie. The movie was specially designed to induce VIMS. The movie was shown for the duration of 10 min to every participant. The electroencephalogram (EEG) data was recorded throughout the session. At the end of the session, participants rated their feelings using simulator sickness questionnaire (SSQ). The SSQ data was analyzed and the ratings of 2D and 3D participants were compared statistically by using a two tailed t test. From the SSQ results, it was found that participants watching 3D movies reported significantly higher symptoms of VIMS (p value EEG data was analyzed by using MATLAB and topographic plots are created from the data. A significant difference has been observed in the frontal-theta power which increases with the passage of time in 2D condition while decreases with time in 3D condition. Also, a decrease in beta power has been found in the temporal lobe of 3D group. Therefore, it is concluded that there are negative effects of 3D movies causing significant changes in the brain activity in terms of band powers. This condition leads to produce symptoms of VIMS in the viewers.
EEG analysis of the brain activity during the observation of commercial, political, or public service announcements.
Vecchiato, Giovanni; Astolfi, Laura; Tabarrini, Alessandro; Salinari, Serenella; Mattia, Donatella; Cincotti, Febo; Bianchi, Luigi; Sorrentino, Domenica; Aloise, Fabio; Soranzo, Ramon; Babiloni, Fabio
The use of modern brain imaging techniques could be useful to understand what brain areas are involved in the observation of video clips related to commercial advertising, as well as for the support of political campaigns, and also the areas of Public Service Announcements (PSAs). In this paper we describe the capability of tracking brain activity during the observation of commercials, political spots, and PSAs with advanced high-resolution EEG statistical techniques in time and frequency domains in a group of normal subjects. We analyzed the statistically significant cortical spectral power activity in different frequency bands during the observation of a commercial video clip related to the use of a beer in a group of 13 normal subjects. In addition, a TV speech of the Prime Minister of Italy was analyzed in two groups of swing and "supporter" voters. Results suggested that the cortical activity during the observation of commercial spots could vary consistently across the spot. This fact suggest the possibility to remove the parts of the spot that are not particularly attractive by using those cerebral indexes. The cortical activity during the observation of the political speech indicated a major cortical activity in the supporters group when compared to the swing voters. In this case, it is possible to conclude that the communication proposed has failed to raise attention or interest on swing voters. In conclusions, high-resolution EEG statistical techniques have been proved to able to generate useful insights about the particular fruition of TV messages, related to both commercial as well as political fields.
Preictal dynamics of EEG complexity in intracranially recorded epileptic seizure: a case report.
Bob, Petr; Roman, Robert; Svetlak, Miroslav; Kukleta, Miloslav; Chladek, Jan; Brazdil, Milan
Recent findings suggest that neural complexity reflecting a number of independent processes in the brain may characterize typical changes during epileptic seizures and may enable to describe preictal dynamics. With respect to previously reported findings suggesting specific changes in neural complexity during preictal period, we have used measure of pointwise correlation dimension (PD2) as a sensitive indicator of nonstationary changes in complexity of the electroencephalogram (EEG) signal. Although this measure of complexity in epileptic patients was previously reported by Feucht et al (Applications of correlation dimension and pointwise dimension for non-linear topographical analysis of focal onset seizures. Med Biol Comput. 1999;37:208-217), it was not used to study changes in preictal dynamics. With this aim to study preictal changes of EEG complexity, we have examined signals from 11 multicontact depth (intracerebral) EEG electrodes located in 108 cortical and subcortical brain sites, and from 3 scalp EEG electrodes in a patient with intractable epilepsy, who underwent preoperative evaluation before epilepsy surgery. From those 108 EEG contacts, records related to 44 electrode contacts implanted into lesional structures and white matter were not included into the experimental analysis.The results show that in comparison to interictal period (at about 8-6 minutes before seizure onset), there was a statistically significant decrease in PD2 complexity in the preictal period at about 2 minutes before seizure onset in all 64 intracranial channels localized in various brain sites that were included into the analysis and in 3 scalp EEG channels as well. Presented results suggest that using PD2 in EEG analysis may have significant implications for research of preictal dynamics and prediction of epileptic seizures.
[Detection of quadratic phase coupling between EEG signal components by nonparamatric and parametric methods of bispectral analysis].
Schmidt, K; Witte, H
Recently the assumption of the independence of individual frequency components in a signal has been rejected, for example, for the EEG during defined physiological states such as sleep or sedation [9, 10]. Thus, the use of higher-order spectral analysis capable of detecting interrelations between individual signal components has proved useful. The aim of the present study was to investigate the quality of various non-parametric and parametric estimation algorithms using simulated as well as true physiological data. We employed standard algorithms available for the MATLAB. The results clearly show that parametric bispectral estimation is superior to non-parametric estimation in terms of the quality of peak localisation and the discrimination from other peaks.
Prediction of rhythmic and periodic EEG patterns and seizures on continuous EEG with early epileptiform discharges.
Koren, J; Herta, J; Draschtak, S; Pötzl, G; Pirker, S; Fürbass, F; Hartmann, M; Kluge, T; Baumgartner, C
Continuous EEG (cEEG) is necessary to document nonconvulsive seizures (NCS), nonconvulsive status epilepticus (NCSE), as well as rhythmic and periodic EEG patterns of 'ictal-interictal uncertainty' (RPPIIU) including periodic discharges, rhythmic delta activity, and spike-and-wave complexes in neurological intensive care patients. However, cEEG is associated with significant recording and analysis efforts. Therefore, predictors from short-term routine EEG with a reasonably high yield are urgently needed in order to select patients for evaluation with cEEG. The aim of this study was to assess the prognostic significance of early epileptiform discharges (i.e., within the first 30 min of EEG recording) on the following: (1) incidence of ictal EEG patterns and RPPIIU on subsequent cEEG, (2) occurrence of acute convulsive seizures during the ICU stay, and (3) functional outcome after 6 months of follow-up. We conducted a separate analysis of the first 30 min and the remaining segments of prospective cEEG recordings according to the ACNS Standardized Critical Care EEG Terminology as well as NCS criteria and review of clinical data of 32 neurological critical care patients. In 17 patients with epileptiform discharges within the first 30 min of EEG (group 1), electrographic seizures were observed in 23.5% (n = 4), rhythmic or periodic EEG patterns of 'ictal-interictal uncertainty' in 64.7% (n = 11), and neither electrographic seizures nor RPPIIU in 11.8% (n = 2). In 15 patients with no epileptiform discharges in the first 30 min of EEG (group 2), no electrographic seizures were recorded on subsequent cEEG, RPPIIU were seen in 26.7% (n = 4), and neither electrographic seizures nor RPPIIU in 73.3% (n = 11). The incidence of EEG patterns on cEEG was significantly different between the two groups (p = 0.008). Patients with early epileptiform discharges developed acute seizures more frequently than patients without early epileptiform discharges (p = 0.009). Finally, functional
Linear and nonlinear analysis of fluid slosh dampers
Sayar, B. A.; Baumgarten, J. R.
A vibrating structure and a container partially filled with fluid are considered coupled in a free vibration mode. To simplify the mathematical analysis, a pendulum model to duplicate the fluid motion and a mass-spring dashpot representing the vibrating structure are used. The equations of motion are derived by Lagrange's energy approach and expressed in parametric form. For a wide range of parametric values the logarithmic decrements of the main system are calculated from theoretical and experimental response curves in the linear analysis. However, for the nonlinear analysis the theoretical and experimental response curves of the main system are compared. Theoretical predictions are justified by experimental observations with excellent agreement. It is concluded finally that for a proper selection of design parameters, containers partially filled with viscous fluids serve as good vibration dampers.
Phase Plane Analysis Method of Nonlinear Traffic Phenomena
Directory of Open Access Journals (Sweden)
Wenhuan Ai
Full Text Available A new phase plane analysis method for analyzing the complex nonlinear traffic phenomena is presented in this paper. This method makes use of variable substitution to transform a traditional traffic flow model into a new model which is suitable for the analysis in phase plane. According to the new model, various traffic phenomena, such as the well-known shock waves, rarefaction waves, and stop-and-go waves, are analyzed in the phase plane. From the phase plane diagrams, we can see the relationship between traffic jams and system instability. So the problem of traffic flow could be converted into that of system stability. The results show that the traffic phenomena described by the new method is consistent with that described by traditional methods. Moreover, the phase plane analysis highlights the unstable traffic phenomena we are chiefly concerned about and describes the variation of density or velocity with time or sections more clearly.
Bearing defect signature analysis using advanced nonlinear signal analysis in a controlled environment
Zoladz, T.; Earhart, E.; Fiorucci, T.
Utilizing high-frequency data from a highly instrumented rotor assembly, seeded bearing defect signatures are characterized using both conventional linear approaches, such as power spectral density analysis, and recently developed nonlinear techniques such as bicoherence analysis. Traditional low-frequency (less than 20 kHz) analysis and high-frequency envelope analysis of both accelerometer and acoustic emission data are used to recover characteristic bearing distress information buried deeply in acquired data. The successful coupling of newly developed nonlinear signal analysis with recovered wideband envelope data from accelerometers and acoustic emission sensors is the innovative focus of this research.
Dipole estimation errors due to not incorporating anisotropic conductivities in realistic head models for EEG source analysis
Hallez, Hans; Staelens, Steven; Lemahieu, Ignace
EEG source analysis is a valuable tool for brain functionality research and for diagnosing neurological disorders, such as epilepsy. It requires a geometrical representation of the human head or a head model, which is often modeled as an isotropic conductor. However, it is known that some brain tissues, such as the skull or white matter, have an anisotropic conductivity. Many studies reported that the anisotropic conductivities have an influence on the calculated electrode potentials. However, few studies have assessed the influence of anisotropic conductivities on the dipole estimations. In this study, we want to determine the dipole estimation errors due to not taking into account the anisotropic conductivities of the skull and/or brain tissues. Therefore, head models are constructed with the same geometry, but with an anisotropically conducting skull and/or brain tissue compartment. These head models are used in simulation studies where the dipole location and orientation error is calculated due to neglecting anisotropic conductivities of the skull and brain tissue. Results show that not taking into account the anisotropic conductivities of the skull yields a dipole location error between 2 and 25 mm, with an average of 10 mm. When the anisotropic conductivities of the brain tissues are neglected, the dipole location error ranges between 0 and 5 mm. In this case, the average dipole location error was 2.3 mm. In all simulations, the dipole orientation error was smaller than 10°. We can conclude that the anisotropic conductivities of the skull have to be incorporated to improve the accuracy of EEG source analysis. The results of the simulation, as presented here, also suggest that incorporation of the anisotropic conductivities of brain tissues is not necessary. However, more studies are needed to confirm these suggestions.
Dipole estimation errors due to not incorporating anisotropic conductivities in realistic head models for EEG source analysis
International Nuclear Information System (INIS)
Hallez, Hans; Staelens, Steven; Lemahieu, Ignace
EEG source analysis is a valuable tool for brain functionality research and for diagnosing neurological disorders, such as epilepsy. It requires a geometrical representation of the human head or a head model, which is often modeled as an isotropic conductor. However, it is known that some brain tissues, such as the skull or white matter, have an anisotropic conductivity. Many studies reported that the anisotropic conductivities have an influence on the calculated electrode potentials. However, few studies have assessed the influence of anisotropic conductivities on the dipole estimations. In this study, we want to determine the dipole estimation errors due to not taking into account the anisotropic conductivities of the skull and/or brain tissues. Therefore, head models are constructed with the same geometry, but with an anisotropically conducting skull and/or brain tissue compartment. These head models are used in simulation studies where the dipole location and orientation error is calculated due to neglecting anisotropic conductivities of the skull and brain tissue. Results show that not taking into account the anisotropic conductivities of the skull yields a dipole location error between 2 and 25 mm, with an average of 10 mm. When the anisotropic conductivities of the brain tissues are neglected, the dipole location error ranges between 0 and 5 mm. In this case, the average dipole location error was 2.3 mm. In all simulations, the dipole orientation error was smaller than 10 deg. We can conclude that the anisotropic conductivities of the skull have to be incorporated to improve the accuracy of EEG source analysis. The results of the simulation, as presented here, also suggest that incorporation of the anisotropic conductivities of brain tissues is not necessary. However, more studies are needed to confirm these suggestions.
Automatic seizure detection: going from sEEG to iEEG
DEFF Research Database (Denmark)
Henriksen, Jonas; Remvig, Line Sofie; Madsen, Rasmus Elsborg
Several different algorithms have been proposed for automatic detection of epileptic seizures based on both scalp and intracranial electroencephalography (sEEG and iEEG). Which modality that renders the best result is hard to assess though. From 16 patients with focal epilepsy, at least 24 hours...... of ictal and non-ictal iEEG were obtained. Characteristics of the seizures are represented by use of wavelet transformation (WT) features and classified by a support vector machine. When implementing a method used for sEEG on iEEG data, a great improvement in performance was obtained when the high...... frequency containing lower levels in the WT were included in the analysis. We were able to obtain a sensitivity of 96.4% and a false detection rate (FDR) of 0.20/h. In general, when implementing an automatic seizure detection algorithm made for sEEG on iEEG, great improvement can be obtained if a frequency...
Nonlinear dynamic analysis of high energy line pipe whip
International Nuclear Information System (INIS)
Hsu, L.C.; Kuo, A.Y.; Tang, H.T.
To facilitate potential cost savings in pipe whip protection design, TVA conducted a 1'' high pressure line break test to investigate the pipe whip behavior. The test results are available to EPRI as a data base for a generic study on nonlinear dynamic behavior of piping systems and pipe whip phenomena. This paper describes a nonlinear dynamic analysis of the TVA high energy line tests using ABAQUS-EPGEN code. The analysis considers the effects of large deformation and high strain rate on resisting moment and energy absorption capability of the analyzed piping system. The numerical results of impact forces, impact velocities, and reaction forces at pipe supports are compared to the TVA test data. The pipe whip impact time and forces have also been calculated per the current NRC guidelines and compared. The calculated pipe support reaction forces prior to impact have been found to be in good agreement with the TVA test data except for some peak values at the very beginning of the pipe break. These peaks are believed to be due to stress wave propagation which cannot be addressed by the ABAQUS code. Both the effects of elbow crushing and strain rate have been approximately simulated. The results are found to be important on pipe whip impact evaluation. (orig.)
Nonlinear analysis of the electroencephalogram in depth of anesthesia
Directory of Open Access Journals (Sweden)
Oscar Leonardo Mosquera-Dussan
Full Text Available El procesamiento digital de la señal de electroencefalograma (EEG ha tomado importancia en el monitoreo de profundidad anestésica, contribuyendo a una mejor técnica anestésica. El objetivo es realizar una revisión de métodos matemáticos no lineales aplicados recientemente al análisis de EEG la cual presenta características no lineales y no estacionarias. Una revisión fue desarrollada abarcando métodos matemáticos no lineales en el dominio del tiempo y frecuencia, los cuales han sido aplicados recientemente al análisis de EEG: Entropía Aproximada, Entropía Muestral, Entropía Espectral, Entropía Permutada, Transformada Wavelet, Entropía Wavelet, Bispectro, Bicoherencia y Transformada Hilbert Huang. Los algoritmos implementados fueron probados en un registro EEG de un paciente en la Clínica Universidad de La Sabana. Resultados publicados en la literatura a fin del tema son discutidos. Técnicas no lineales como el análisis de entropía, y la combinación con transformadas Wavelet y Hilbert Huang en el dominio de la frecuencia han presentado resultados prometedores en clasificación de estados de profundidad anestésica.
Dual Solutions for Nonlinear Flow Using Lie Group Analysis.
Directory of Open Access Journals (Sweden)
Muhammad Awais
Full Text Available `The aim of this analysis is to investigate the existence of the dual solutions for magnetohydrodynamic (MHD flow of an upper-convected Maxwell (UCM fluid over a porous shrinking wall. We have employed the Lie group analysis for the simplification of the nonlinear differential system and computed the absolute invariants explicitly. An efficient numerical technique namely the shooting method has been employed for the constructions of solutions. Dual solutions are computed for velocity profile of an upper-convected Maxwell (UCM fluid flow. Plots reflecting the impact of dual solutions for the variations of Deborah number, Hartman number, wall mass transfer are presented and analyzed. Streamlines are also plotted for the wall mass transfer effects when suction and blowing situations are considered.
Fractional-Order Nonlinear Systems Modeling, Analysis and Simulation
Petráš, Ivo
"Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation" presents a study of fractional-order chaotic systems accompanied by Matlab programs for simulating their state space trajectories, which are shown in the illustrations in the book. Description of the chaotic systems is clearly presented and their analysis and numerical solution are done in an easy-to-follow manner. Simulink models for the selected fractional-order systems are also presented. The readers will understand the fundamentals of the fractional calculus, how real dynamical systems can be described using fractional derivatives and fractional differential equations, how such equations can be solved, and how to simulate and explore chaotic systems of fractional order. The book addresses to mathematicians, physicists, engineers, and other scientists interested in chaos phenomena or in fractional-order systems. It can be used in courses on dynamical systems, control theory, and applied mathematics at graduate or postgraduate level. ...
Analysis of nonlinear behavior of loudspeakers using the instantaneous frequency
DEFF Research Database (Denmark)
Huang, Hai; Jacobsen, Finn
on the Fourier transform. In this work, a new method using the instantaneous frequency is introduced for describing and characterizing loudspeaker nonlinearities. First, numerical integration is applied to simulate the nonlinearities of loudspeakers caused by two nonlinear parameters, force factor and stiffness...
Applications of Automation Methods for Nonlinear Fracture Test Analysis
Allen, Phillip A.; Wells, Douglas N.
Using automated and standardized computer tools to calculate the pertinent test result values has several advantages such as: 1. allowing high-fidelity solutions to complex nonlinear phenomena that would be impractical to express in written equation form, 2. eliminating errors associated with the interpretation and programing of analysis procedures from the text of test standards, 3. lessening the need for expertise in the areas of solid mechanics, fracture mechanics, numerical methods, and/or finite element modeling, to achieve sound results, 4. and providing one computer tool and/or one set of solutions for all users for a more "standardized" answer. In summary, this approach allows a non-expert with rudimentary training to get the best practical solution based on the latest understanding with minimum difficulty.Other existing ASTM standards that cover complicated phenomena use standard computer programs: 1. ASTM C1340/C1340M-10- Standard Practice for Estimation of Heat Gain or Loss Through Ceilings Under Attics Containing Radiant Barriers by Use of a Computer Program 2. ASTM F 2815 - Standard Practice for Chemical Permeation through Protective Clothing Materials: Testing Data Analysis by Use of a Computer Program 3. ASTM E2807 - Standard Specification for 3D Imaging Data Exchange, Version 1.0 The verification, validation, and round-robin processes required of a computer tool closely parallel the methods that are used to ensure the solution validity for equations included in test standard. The use of automated analysis tools allows the creation and practical implementation of advanced fracture mechanics test standards that capture the physics of a nonlinear fracture mechanics problem without adding undue burden or expense to the user. The presented approach forms a bridge between the equation-based fracture testing standards of today and the next generation of standards solving complex problems through analysis automation.
Fractional analysis for nonlinear electrical transmission line and nonlinear Schroedinger equations with incomplete sub-equation
Fendzi-Donfack, Emmanuel; Nguenang, Jean Pierre; Nana, Laurent
We use the fractional complex transform with the modified Riemann-Liouville derivative operator to establish the exact and generalized solutions of two fractional partial differential equations. We determine the solutions of fractional nonlinear electrical transmission lines (NETL) and the perturbed nonlinear Schroedinger (NLS) equation with the Kerr law nonlinearity term. The solutions are obtained for the parameters in the range (0<α≤1) of the derivative operator and we found the traditional solutions for the limiting case of α =1. We show that according to the modified Riemann-Liouville derivative, the solutions found can describe physical systems with memory effect, transient effects in electrical systems and nonlinear transmission lines, and other systems such as optical fiber.
An algorithm for nonlinear thermal analysis of fuel bearing pads
International Nuclear Information System (INIS)
Attia, M.H.; D'Silva, N.
An algorithm has been developed for accurate prediction of the temperature field in a CANDU fuel bearing pad and the extent of the nucleate boiling in the crevice region. The methodology recognizes the nonlinear nature of the problem due to the fact that local boiling is both controlling and being controlled by the conditions of heat transfer at the boundaries. The finite difference model accounts for the volumetric effect of the thermal contact resistance at the bearing pad/pressure tube interface. It also allows the evaluation of the thermal barrier effect caused by applying an oxide film on the radiused surface of the bearing pad. Information pertaining to the distribution of the coefficient of heat transfer over water-cooled surfaces has been generated. Analysis of the results indicated the significance of considering the nonlinear behaviour of the system in determining its state of equilibrium. It also indicated that, depending on the thickness of the oxide layer and the position of the bearing pad along the core of the reactor, the nucleate boiling process can be prevented
Nonlinear seismic analysis of a graphite reactor core
International Nuclear Information System (INIS)
Laframboise, W.L.; Desmond, T.P.
Design and construction of the Department of Energy's N-Reactor located in Richland, Washington was begun in the late 1950s and completed in the early 1960s. Since then, the reactor core's structural integrity has been under review and is considered by some to be a possible safety concern. The reactor core is moderated by graphite. The safety concern stems from the degradation of the graphite due to the effects of long-term irradiation. To assess the safety of the reactor core when subjected to seismic loads, a dynamic time-history structural analysis was performed. The graphite core consists of 89 layers of numerous graphite blocks which are assembled in a 'lincoln-log' lattice. This assembly permits venting of steam in the event of a pressure tube rupture. However, such a design gives rise to a highly nonlinear structure when subjected to earthquake loads. The structural model accounted for the nonlinear interlayer sliding and for the closure and opening of gaps between the graphite blocks. The model was subjected to simulated earthquake loading, and the time-varying response of selected elements critical to safety were monitored. The analytically predicted responses (displacements and strains) were compared to allowable responses to assess margins of safety. (orig.)
A nonlinear 3D containment analysis for airplane impact
International Nuclear Information System (INIS)
Buchhardt, F.; Magiera, G.; Matthees, W.; Weber, M.
In the Federal Republic of Germany, it is pertinent safety philosophy to design nuclear facilities against airplane impact, despite its very unlikely probability of occurrence. For safety reasons, the following conditions have to be met: 1) In the close impact area of the projectile, the structure can be stressed up to its ultimate load capacity, so that impact energy is dissipated partly. Hereby, it must be strictly clarified that local structural failure within the impact zone is avoided. 2) Residual impact energy is transferred to the 'non-disturbed' containment structure and to the interior structure. The subject of reinforced concrete structures under impact loads shows still clear gaps between the findings of experimental and analytical analyses. To clarify this highly nonlinear phenomena comprehensive tests have recently been performed in Germany. It is the aim of this paper to carry out a three-dimensional analysis of a nuclear facility. To perform the calculations, the finite element ADINA code is applied. In order to obtain optimum results, a very fine mesh leading to several thousand DOF is used. To model the impact area of the concrete structure realistically, its linear and mostly nonlinear material behaviour as well as its failure criteria must be taken into account. Herewith the structural response is reduced due to increased energy dissipation. This reduction rate is valued by variation of the assumed size of impact zone, the load impact location and the assumed load-time function. (orig./RW)
Nonlinear analysis of prestressed concrete reactor pressure vessels
International Nuclear Information System (INIS)
Connor, J.J.
The numerical procedures for predicting the nonlinear behavior of a prestressed concrete reactor vessel over its design life are discussed. The numerical models are constructed by combining three-dimensional isoparametric finite elements which simulate the concrete, thin shell elements which simulate steel linear plates, and layers of reinforcement steel, and axial elements for discrete prestressing cables. Nonlinearity under compressive stress, multi-dimensional cracking, shrinkage and stress/temperature induced creep of concrete are considered in addition to the elasti-plastic behavior of the liner and reinforcing steel. Various failure theories for concrete have been proposed recently. Also, there are alternative strategies for solving the discrete system equations over the design life, accounting for test loads, pressure and temperature operational loads, creep unloading and abnormal loads. The proposed methods are reviewed, and a new formulation developed by the authors is described. A number of comparisons with experimental tests results and other numerical schemes are presented. These examples demonstrate the validity of the formulation and also provide valuable information concerning the cost and accuracy of the various solution strategies i.e., total vs. incremental loading and initial vs. tangent stiffness. Finally, the analysis of an actual PCRV is described. Stress contours and cracking patterns in the region of cutouts corresponding to operational pressure and temperature loads are illustrated. The effects of creep, unloading, and creep recovery are then shown. Lastly, a strategy for assessing the performance over its design life is discussed
Nonlinear analysis for dual-frequency concurrent energy harvesting
Yan, Zhimiao; Lei, Hong; Tan, Ting; Sun, Weipeng; Huang, Wenhu
The dual-frequency responses of the hybrid energy harvester undergoing the base excitation and galloping were analyzed numerically. In this work, an approximate dual-frequency analytical method is proposed for the nonlinear analysis of such a system. To obtain the approximate analytical solutions of the full coupled distributed-parameter model, the forcing interactions is first neglected. Then, the electromechanical decoupled governing equation is developed using the equivalent structure method. The hybrid mechanical response is finally separated to be the self-excited and forced responses for deriving the analytical solutions, which are confirmed by the numerical simulations of the full coupled model. The forced response has great impacts on the self-excited response. The boundary of Hopf bifurcation is analytically determined by the onset wind speed to galloping, which is linearly increased by the electrical damping. Quenching phenomenon appears when the increasing base excitation suppresses the galloping. The theoretical quenching boundary depends on the forced mode velocity. The quenching region increases with the base acceleration and electrical damping, but decreases with the wind speed. Superior to the base-excitation-alone case, the existence of the aerodynamic force protects the hybrid energy harvester at resonance from damages caused by the excessive large displacement. From the view of the harvested power, the hybrid system surpasses the base-excitation-alone system or the galloping-alone system. This study advances our knowledge on intrinsic nonlinear dynamics of the dual-frequency energy harvesting system by taking advantage of the analytical solutions.
Flutter analysis of an airfoil with multiple nonlinearities and uncertainties
Directory of Open Access Journals (Sweden)
Haitao Liao
Full Text Available An original method for calculating the limit cycle oscillations of nonlinear aero-elastic system is presented. The problem of determining the maximum vibration amplitude of limit cycle is transformed into a nonlinear optimization problem. The harmonic balance method and the Floquet theory are selected to construct the general nonlinear equality and inequality constraints. The resulting constrained maximization problem is then solved by using the MultiStart algorithm. Finally, the proposed approach is validated and used to analyse the limit cycle oscillations of an airfoil with multiple nonlinearities and uncertainties. Numerical examples show that the coexistence of multiple nonlinearities may lead to low amplitude limit cycle oscillation.
Modeling epileptic brain states using EEG spectral analysis and topographic mapping.
Direito, Bruno; Teixeira, César; Ribeiro, Bernardete; Castelo-Branco, Miguel; Sales, Francisco; Dourado, António
Changes in the spatio-temporal behavior of the brain electrical activity are believed to be associated to epileptic brain states. We propose a novel methodology to identify the different states of the epileptic brain, based on the topographic mapping of the time varying relative power of delta, theta, alpha, beta and gamma frequency sub-bands, estimated from EEG. Using normalized-cuts segmentation algorithm, points of interest are identified in the topographic mappings and their trajectories over time are used for finding out relations with epileptogenic propagations in the brain. These trajectories are used to train a Hidden Markov Model (HMM), which models the different epileptic brain states and the transition among them. Applied to 10 patients suffering from focal seizures, with a total of 30 seizures over 497.3h of data, the methodology shows good results (an average point-by-point accuracy of 89.31%) for the identification of the four brain states--interictal, preictal, ictal and postictal. The results suggest that the spatio-temporal dynamics captured by the proposed methodology are related to the epileptic brain states and transitions involved in focal seizures. Copyright © 2012 Elsevier B.V. All rights reserved.
Nonlinear analysis and characteristics of inductive galloping energy harvesters
Dai, H. L.; Yang, Y. W.; Abdelkefi, A.; Wang, L.
This paper presents an investigation on analysis and characteristics of aerodynamic electromagnetic energy harvesters. The source of aeroelastic oscillations results from galloping of a prismatic structure. A nonlinear distributed-parameter model is developed representing the dynamics of the transverse degree of freedom and the electric current induced in the coil. Firstly, we perform a linear analysis to study the impacts of the external electrical resistance, magnet placement, electromagnetic coupling coefficient, and internal resistance in the coil on the cut-in speed of instability of the coupled electroaeroelastic system. It is demonstrated that these parameters have significant impacts on cut-in speed of instability of the harvester system. Subsequently, a nonlinear analysis is implemented to explore the influences of these parameters on the output property of the energy harvester. The results show that there exists an optimal external electrical resistance which maximizes the output power of the harvester, and this optimal value varies with the magnet's placement, wind speed, electromagnetic coupling coefficient and internal resistance of the coil. It is also demonstrated that an increase in the distance between the clamped end and the magnet, an increase in the electromagnetic coupling coefficient, and/or a decrease in the internal resistance of the coil are accompanied by an increase in the level of the harvested power and a decrease in the tip displacement of the bluff body which is associated with a resistive-shunt damping effect in the harvester. The implemented studies give a constructive guidance to design and enhance the output performance of aerodynamic electromagnetic energy harvesters.
Comparative analysis of MR imaging, Ictal SPECT and EEG in temporal lobe epilepsy: a prospective IAEA multi-center study
Energy Technology Data Exchange (ETDEWEB)
Zaknun, John J. [University Hospital of Innsbruck, Department of Nuclear Medicine, Innsbruck (Austria); International Atomic Energy Agency (IAEA), Nuclear Medicine Section, Division of Human Health, Vienna (Austria); IAEA, Nuclear Medicine Section, Division of Human Health, Wagramer Strasse 5, P.O. Box 100, Wien (Austria); Bal, Chandrasekhar [All India Institute of Medical Sciences, Department of Nuclear Medicine, New Delhi (India); Maes, Alex [Katholieke Universiteit Leuven, Leuven (Belgium); AZ Groeninge, Department of Nuclear Medicine, Kortrijk (Belgium); Tepmongkol, Supatporn [Chulalongkorn University, Nuclear Medicine Division, Department of Radiology, Bangkok (Thailand); Vazquez, Silvia [Instituto de Investigaciones Neurologicas, FLENI, Department of Radiology, Buenos Aires (Argentina); Dupont, Patrick [Katholieke Universiteit Leuven, Leuven (Belgium); Dondi, Maurizio [Ospedale Maggiore, Department of Nuclear Medicine, Bologna (Italy); International Atomic Energy Agency (IAEA), Nuclear Medicine Section, Division of Human Health, Vienna (Austria)
MR imaging, ictal single-photon emission CT (SPECT) and ictal EEG play important roles in the presurgical localization of epileptic foci. This multi-center study was established to investigate whether the complementary role of perfusion SPECT, MRI and EEG for presurgical localization of temporal lobe epilepsy could be confirmed in a prospective setting involving centers from India, Thailand, Italy and Argentina. We studied 74 patients who underwent interictal and ictal EEG, interictal and ictal SPECT and MRI before surgery of the temporal lobe. In all but three patients, histology was reported. The clinical outcome was assessed using Engel's classification. Sensitivity values of all imaging modalities were calculated, and the add-on value of SPECT was assessed. Outcome (Engel's classification) in 74 patients was class I, 89%; class II, 7%; class III, 3%; and IV, 1%. Regarding the localization of seizure origin, sensitivity was 84% for ictal SPECT, 70% for ictal EEG, 86% for MRI, 55% for interictal SPECT and 40% for interictal EEG. Add-on value of ictal SPECT was shown by its ability to correctly localize 17/22 (77%) of the seizure foci missed by ictal EEG and 8/10 (80%) of the seizure foci not detected by MRI. This prospective multi-center trial, involving centers from different parts of the world, confirms that ictal perfusion SPECT is an effective diagnostic modality for correctly identifying seizure origin in temporal lobe epilepsy, providing complementary information to ictal EEG and MRI. (orig.)
Comparative analysis of MR imaging, Ictal SPECT and EEG in temporal lobe epilepsy: a prospective IAEA multi-center study
International Nuclear Information System (INIS)
Zaknun, John J.; Bal, Chandrasekhar; Maes, Alex; Tepmongkol, Supatporn; Vazquez, Silvia; Dupont, Patrick; Dondi, Maurizio
MR imaging, ictal single-photon emission CT (SPECT) and ictal EEG play important roles in the presurgical localization of epileptic foci. This multi-center study was established to investigate whether the complementary role of perfusion SPECT, MRI and EEG for presurgical localization of temporal lobe epilepsy could be confirmed in a prospective setting involving centers from India, Thailand, Italy and Argentina. We studied 74 patients who underwent interictal and ictal EEG, interictal and ictal SPECT and MRI before surgery of the temporal lobe. In all but three patients, histology was reported. The clinical outcome was assessed using Engel's classification. Sensitivity values of all imaging modalities were calculated, and the add-on value of SPECT was assessed. Outcome (Engel's classification) in 74 patients was class I, 89%; class II, 7%; class III, 3%; and IV, 1%. Regarding the localization of seizure origin, sensitivity was 84% for ictal SPECT, 70% for ictal EEG, 86% for MRI, 55% for interictal SPECT and 40% for interictal EEG. Add-on value of ictal SPECT was shown by its ability to correctly localize 17/22 (77%) of the seizure foci missed by ictal EEG and 8/10 (80%) of the seizure foci not detected by MRI. This prospective multi-center trial, involving centers from different parts of the world, confirms that ictal perfusion SPECT is an effective diagnostic modality for correctly identifying seizure origin in temporal lobe epilepsy, providing complementary information to ictal EEG and MRI. (orig.)
Analysis of Nonlinear Duopoly Game: A Cooperative Case
Directory of Open Access Journals (Sweden)
S. S. Askar
Full Text Available We make further attempts to investigate equilibrium stability of a nonlinear Cournot duopoly game. Our studies in this paper focus on the cooperation that may be obtained among duopolistic firms. Discrete time scales under the assumption of unknown inverse demand function and linear cost are used to build our models in the proposed games. We introduce and study here an adjustment dynamic strategy beside the so-called tit-for-tat strategy. For each model, the stability analysis of the fixed point is analyzed. Numerical simulations are carried out to show the complex behavior of the proposed models and to point out the impact of the models' parameters on the cooperation.
Recurrence Density Enhanced Complex Networks for Nonlinear Time Series Analysis
Costa, Diego G. De B.; Reis, Barbara M. Da F.; Zou, Yong; Quiles, Marcos G.; Macau, Elbert E. N.
We introduce a new method, which is entitled Recurrence Density Enhanced Complex Network (RDE-CN), to properly analyze nonlinear time series. Our method first transforms a recurrence plot into a figure of a reduced number of points yet preserving the main and fundamental recurrence properties of the original plot. This resulting figure is then reinterpreted as a complex network, which is further characterized by network statistical measures. We illustrate the computational power of RDE-CN approach by time series by both the logistic map and experimental fluid flows, which show that our method distinguishes different dynamics sufficiently well as the traditional recurrence analysis. Therefore, the proposed methodology characterizes the recurrence matrix adequately, while using a reduced set of points from the original recurrence plots.
Nonlinear analysis of the GFRP material wheel hub
Directory of Open Access Journals (Sweden)
Dong Yun-Feng
Full Text Available In this paper, the current bicycle wheel was replaced by the ones which composed by the wheel hub with Glassfiber Reinforced Plastic (alkali free thin-walled cylinder material, hereinafter referred to as GFRP material and the protective components made up of rubber outer pneumatic pad. With the help of the basic theory of elastic-plastic mechanics, the finite element "Nonlinear buckling" analysis of the wheel was carried out. The results show that the maximum elastic deformation of the wheel hub and the critical value of buckling failure load were restricted by the elasticity under the condition of external loads. Considering with the tensile strength and elastic modulus of the GFRP value of the material, it is demonstrated that the material is feasible to be used for wheel hub.
Extended seizure detection algorithm for intracranial EEG recordings
DEFF Research Database (Denmark)
Kjaer, T. W.; Remvig, L. S.; Henriksen, J.
Objective: We implemented and tested an existing seizure detection algorithm for scalp EEG (sEEG) with the purpose of improving it to intracranial EEG (iEEG) recordings. Method: iEEG was obtained from 16 patients with focal epilepsy undergoing work up for resective epilepsy surgery. Each patient...... had 4 or 5 recorded seizures and 24 hours of non-ictal data were used for evaluation. Data from three electrodes placed at the ictal focus were used for the analysis. A wavelet based feature extraction algorithm delivered input to a support vector machine (SVM) classifier for distinction between ictal...... and non-ictal iEEG. We compare our results to a method published by Shoeb in 2004. While the original method on sEEG was optimal with the use of only four subbands in the wavelet analysis, we found that better seizure detection could be made if all subbands were used for iEEG. Results: When using...
Applying Improved Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG
Directory of Open Access Journals (Sweden)
Ming-ai Li
Full Text Available Electroencephalography (EEG is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG not only has a close correlation with the human imagination and movement intention but also contains a large amount of physiological or disease information. As a result, it has been fully studied in the field of rehabilitation. To correctly interpret and accurately extract the features of MI-EEG signals, many nonlinear dynamic methods based on entropy, such as Approximate Entropy (ApEn, Sample Entropy (SampEn, Fuzzy Entropy (FE, and Permutation Entropy (PE, have been proposed and exploited continuously in recent years. However, these entropy-based methods can only measure the complexity of MI-EEG based on a single scale and therefore fail to account for the multiscale property inherent in MI-EEG. To solve this problem, Multiscale Sample Entropy (MSE, Multiscale Permutation Entropy (MPE, and Multiscale Fuzzy Entropy (MFE are developed by introducing scale factor. However, MFE has not been widely used in analysis of MI-EEG, and the same parameter values are employed when the MFE method is used to calculate the fuzzy entropy values on multiple scales. Actually, each coarse-grained MI-EEG carries the characteristic information of the original signal on different scale factors. It is necessary to optimize MFE parameters to discover more feature information. In this paper, the parameters of MFE are optimized independently for each scale factor, and the improved MFE (IMFE is applied to the feature extraction of MI-EEG. Based on the event-related desynchronization (ERD/event-related synchronization (ERS phenomenon, IMFE features from multi channels are fused organically to construct the feature vector. Experiments are conducted on a public dataset by using Support Vector Machine (SVM as a classifier. The experiment results of 10-fold cross-validation show that the proposed method yields
Nonlinear effects in dynamic analysis and design of nuclear power plant components: research status and needs
Energy Technology Data Exchange (ETDEWEB)
Stoykovich, M [Burns and Roe, Inc., New York (USA)
This paper encompasses nonlinear effects in dynamic analysis and design of nuclear power plant facilities. The history of plasticity as a science is briefly discussed, and nonlinear cases of special interest are described. Approaches to some of the nonlinear problems are presented. These include the nonlinearity due to foundation-structure interaction associated with the base slab uplift during seismic disturbances, the nonlinear base-isolation system for the reduction of earthquake-generated forces and deformations of superstructures, nonlinear systems having restoring-force functions in case of gaps and liift-off conditions, and nonlinearity of viscoelastic systems due to inelastic deformations. Available computer programs information for the solution of various types of nonlinear problems are provided. Advantages and disadvantages of some of the nonlinear and linear analyses are discussed. Comparison of some nonlinear and linear results of analyses are presented. Conclusions are reached with regard to research status and recommendations for further studies and for performing non-linear analyses associated with the problems of nonlinearity are presented.
Nonlinear effects in dynamic analysis and design of nuclear power plant components: research status and needs
International Nuclear Information System (INIS)
Stoykovich, M.
This paper encompasses nonlinear effects in dynamic analysis and design of nuclear power plant facilities. The history of plasticity as a science is briefly discussed, and nonlinear cases of special interest are described. Approaches to some of the nonlinear problems are presented. These include the nonlinearity due to foundation-structure interaction associated with the base slab uplift during seismic disturbances, the nonlinear base-isolation system for the reduction of earthquake-generated forces and deformations of superstructures, nonlinear systems having restoring-force functions in case of gaps and liift-off conditions, and nonlinearity of viscoelastic systems due to inelastic deformations. Available computer programs information for the solution of various types of nonlinear problems are provided. Advantages and disadvantages of some of the nonlinear and linear analyses are discussed. Comparison of some nonlinear and linear results of analyses are presented. Conclusions are reached with regard to research status and recommendations for further studies and for performing non-linear analyses associated with the problems of nonlinearity are presented. (Auth.)
Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction
de Munck, J.C.; van Houdt, P.J.; Goncalves, S.I.; van Wegen, E.E.H.; Ossenblok, P.P.W.
Co-registered EEG and functional MRI (EEG/fMRI) is a potential clinical tool for planning invasive EEG in patients with epilepsy. In addition, the analysis of EEG/fMRI data provides a fundamental insight into the precise physiological meaning of both fMRI and EEG data. Routine application of
Item parameters dissociate between expectation formats: A regression analysis of time-frequency decomposed EEG data
Directory of Open Access Journals (Sweden)
Irene Fernández Monsalve
Full Text Available During language comprehension, semantic contextual information is used to generate expectations about upcoming items. This has been commonly studied through the N400 event-related potential (ERP, as a measure of facilitated lexical retrieval. However, the associative relationships in multi-word expressions (MWE may enable the generation of a categorical expectation, leading to lexical retrieval before target word onset. Processing of the target word would thus reflect a target-identification mechanism, possibly indexed by a P3 ERP component. However, given their time overlap (200-500 ms post-stimulus onset, differentiating between N400/P3 ERP responses (averaged over multiple linguistically variable trials is problematic. In the present study, we analyzed EEG data from a previous experiment, which compared ERP responses to highly expected words that were placed either in a MWE or a regular non-fixed compositional context, and to low predictability controls. We focused on oscillatory dynamics and regression analyses, in order to dissociate between the two contexts by modeling the electrophysiological response as a function of item-level parameters. A significant interaction between word position and condition was found in the regression model for power in a theta range (~7-9 Hz, providing evidence for the presence of qualitative differences between conditions. Power levels within this band were lower for MWE than compositional contexts then the target word appeared later on in the sentence, confirming that in the former lexical retrieval would have taken place before word onset. On the other hand, gamma-power (~50-70 Hz was also modulated by predictability of the item in all conditions, which is interpreted as an index of a similar `matching' sub-step for both types of contexts, binding an expected representation and the external input.
Comparison of data transformation procedures to enhance topographical accuracy in time-series analysis of the human EEG.
Hauk, O; Keil, A; Elbert, T; Müller, M M
We describe a methodology to apply current source density (CSD) and minimum norm (MN) estimation as pre-processing tools for time-series analysis of single trial EEG data. The performance of these methods is compared for the case of wavelet time-frequency analysis of simulated gamma-band activity. A reasonable comparison of CSD and MN on the single trial level requires regularization such that the corresponding transformed data sets have similar signal-to-noise ratios (SNRs). For region-of-interest approaches, it should be possible to optimize the SNR for single estimates rather than for the whole distributed solution. An effective implementation of the MN method is described. Simulated data sets were created by modulating the strengths of a radial and a tangential test dipole with wavelets in the frequency range of the gamma band, superimposed with simulated spatially uncorrelated noise. The MN and CSD transformed data sets as well as the average reference (AR) representation were subjected to wavelet frequency-domain analysis, and power spectra were mapped for relevant frequency bands. For both CSD and MN, the influence of noise can be sufficiently suppressed by regularization to yield meaningful information, but only MN represents both radial and tangential dipole sources appropriately as single peaks. Therefore, when relating wavelet power spectrum topographies to their neuronal generators, MN should be preferred.
Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects
Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang
Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.
A Multiscale, Nonlinear, Modeling Framework Enabling the Design and Analysis of Composite Materials and Structures
Bednarcyk, Brett A.; Arnold, Steven M.
A framework for the multiscale design and analysis of composite materials and structures is presented. The ImMAC software suite, developed at NASA Glenn Research Center, embeds efficient, nonlinear micromechanics capabilities within higher scale structural analysis methods such as finite element analysis. The result is an integrated, multiscale tool that relates global loading to the constituent scale, captures nonlinearities at this scale, and homogenizes local nonlinearities to predict their effects at the structural scale. Example applications of the multiscale framework are presented for the stochastic progressive failure of a SiC/Ti composite tensile specimen and the effects of microstructural variations on the nonlinear response of woven polymer matrix composites.
Detection method of nonlinearity errors by statistical signal analysis in heterodyne Michelson interferometer.
Hu, Juju; Hu, Haijiang; Ji, Yinghua
Periodic nonlinearity that ranges from tens of nanometers to a few nanometers in heterodyne interferometer limits its use in high accuracy measurement. A novel method is studied to detect the nonlinearity errors based on the electrical subdivision and the analysis method of statistical signal in heterodyne Michelson interferometer. Under the movement of micropositioning platform with the uniform velocity, the method can detect the nonlinearity errors by using the regression analysis and Jackknife estimation. Based on the analysis of the simulations, the method can estimate the influence of nonlinearity errors and other noises for the dimensions measurement in heterodyne Michelson interferometer.
EEG frequency PCA in EEG-ERP dynamics.
Barry, Robert J; De Blasio, Frances M
Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG-ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes-closed and eyes-open resting conditions, followed by an equiprobable go/no-go task. Frequency PCA of the EEG, including the nontask resting and within-task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA-derived go and no-go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data-driven components from both the ERP and EEG. © 2017 Society for Psychophysiological Research.
The Realistic Versus the Spherical Head Model in EEG Dipole Source Analysis in the Presence of Noise
National Research Council Canada - National Science Library
Vanrumste, Bart
.... For 27 electrodes, an EEG epoch of one time sample and spatially white Gaussian noise we found that the importance of the realistic head model over the spherical head model reduces by increasing the noise level.
Joint time-frequency analysis of EEG signals based on a phase-space interpretation of the recording process
Testorf, M. E.; Jobst, B. C.; Kleen, J. K.; Titiz, A.; Guillory, S.; Scott, R.; Bujarski, K. A.; Roberts, D. W.; Holmes, G. L.; Lenck-Santini, P.-P.
Time-frequency transforms are used to identify events in clinical EEG data. Data are recorded as part of a study for correlating the performance of human subjects during a memory task with pathological events in the EEG, called spikes. The spectrogram and the scalogram are reviewed as tools for evaluating spike activity. A statistical evaluation of the continuous wavelet transform across trials is used to quantify phase-locking events. For simultaneously improving the time and frequency resolution, and for representing the EEG of several channels or trials in a single time-frequency plane, a multichannel matching pursuit algorithm is used. Fundamental properties of the algorithm are discussed as well as preliminary results, which were obtained with clinical EEG data.
Nonlinear Radon Transform Using Zernike Moment for Shape Analysis
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Ziping Ma
Full Text Available We extend the linear Radon transform to a nonlinear space and propose a method by applying the nonlinear Radon transform to Zernike moments to extract shape descriptors. These descriptors are obtained by computing Zernike moment on the radial and angular coordinates of the pattern image's nonlinear Radon matrix. Theoretical and experimental results validate the effectiveness and the robustness of the method. The experimental results show the performance of the proposed method in the case of nonlinear space equals or outperforms that in the case of linear Radon.
West, Michael
Previous research on meditation and the EEG is described, and findings relating to EEG patterns during meditation are discussed. Comparisons of meditation with other altered states are reviewed and it is concluded that, on the basis of existing EEG evidence, there is some reason for differentiating between meditation and drowsing. Research on alpha-blocking and habituation of the blocking response during meditation is reviewed, and the effects of meditation on EEG patterns outside of meditati...
Painleve analysis, conservation laws, and symmetry of perturbed nonlinear equations
International Nuclear Information System (INIS)
Basak, S.; Chowdhury, A.R.
The authors consider the Lie-Backlund symmetries and conservation laws of a perturbed KdV equation and NLS equation. The arbitrary coefficients of the perturbing terms can be related to the condition of existence of nontrivial LB symmetry generators. When the perturbed KdV equation is subjected to Painleve analysis a la Weiss, it is found that the resonance position changes compared to the unperturbed one. They prove the compatibility of the overdetermined set of equations obtained at the different stages of recursion relations, at least for one branch. All other branches are also indicated and difficulties associated them are discussed considering the perturbation parameter epsilon to be small. They determine the Lax pair for the aforesaid branch through the use of Schwarzian derivative. For the perturbed NLS equation they determine the conservation laws following the approach of Chen and Liu. From the recurrence of these conservation laws a Lax pair is constructed. But the Painleve analysis does not produce a positive answer for the perturbed NLS equation. So here they have two contrasting examples of perturbed nonlinear equations: one passes the Painleve test and its Lax pair can be found from the analysis itself, but the other equation does not meet the criterion of the Painleve test, though its Lax pair is found in another way
Assessing Spontaneous Combustion Instability with Nonlinear Time Series Analysis
Eberhart, C. J.; Casiano, M. J.
Considerable interest lies in the ability to characterize the onset of spontaneous instabilities within liquid propellant rocket engine (LPRE) combustion devices. Linear techniques, such as fast Fourier transforms, various correlation parameters, and critical damping parameters, have been used at great length for over fifty years. Recently, nonlinear time series methods have been applied to deduce information pertaining to instability incipiency hidden in seemingly stochastic combustion noise. A technique commonly used in biological sciences known as the Multifractal Detrended Fluctuation Analysis has been extended to the combustion dynamics field, and is introduced here as a data analysis approach complementary to linear ones. Advancing, a modified technique is leveraged to extract artifacts of impending combustion instability that present themselves a priori growth to limit cycle amplitudes. Analysis is demonstrated on data from J-2X gas generator testing during which a distinct spontaneous instability was observed. Comparisons are made to previous work wherein the data were characterized using linear approaches. Verification of the technique is performed by examining idealized signals and comparing two separate, independently developed tools.
Nonlinear Transient Thermal Analysis by the Force-Derivative Method
Balakrishnan, Narayani V.; Hou, Gene
High-speed vehicles such as the Space Shuttle Orbiter must withstand severe aerodynamic heating during reentry through the atmosphere. The Shuttle skin and substructure are constructed primarily of aluminum, which must be protected during reentry with a thermal protection system (TPS) from being overheated beyond the allowable temperature limit, so that the structural integrity is maintained for subsequent flights. High-temperature reusable surface insulation (HRSI), a popular choice of passive insulation system, typically absorbs the incoming radiative or convective heat at its surface and then re-radiates most of it to the atmosphere while conducting the smallest amount possible to the structure by virtue of its low diffusivity. In order to ensure a successful thermal performance of the Shuttle under a prescribed reentry flight profile, a preflight reentry heating thermal analysis of the Shuttle must be done. The surface temperature profile, the transient response of the HRSI interior, and the structural temperatures are all required to evaluate the functioning of the HRSI. Transient temperature distributions which identify the regions of high temperature gradients, are also required to compute the thermal loads for a structural thermal stress analysis. Furthermore, a nonlinear analysis is necessary to account for the temperature-dependent thermal properties of the HRSI as well as to model radiation losses.
Importance measures in global sensitivity analysis of nonlinear models
International Nuclear Information System (INIS)
Homma, Toshimitsu; Saltelli, Andrea
The present paper deals with a new method of global sensitivity analysis of nonlinear models. This is based on a measure of importance to calculate the fractional contribution of the input parameters to the variance of the model prediction. Measures of importance in sensitivity analysis have been suggested by several authors, whose work is reviewed in this article. More emphasis is given to the developments of sensitivity indices by the Russian mathematician I.M. Sobol'. Given that Sobol' treatment of the measure of importance is the most general, his formalism is employed throughout this paper where conceptual and computational improvements of the method are presented. The computational novelty of this study is the introduction of the 'total effect' parameter index. This index provides a measure of the total effect of a given parameter, including all the possible synergetic terms between that parameter and all the others. Rank transformation of the data is also introduced in order to increase the reproducibility of the method. These methods are tested on a few analytical and computer models. The main conclusion of this work is the identification of a sensitivity analysis methodology which is both flexible, accurate and informative, and which can be achieved at reasonable computational cost
Nonlinear analysis of collapse mechanism in superstructure vehicle
Nor, M. K. Mohd; Ho, C. S.; Ma'at, N.
The EU directive 2001/85/EC is an official European text which describes the specifications for "single deck class II and III vehicles" required to be approved by the regulation UN/ECE no.66 (R66). To prevent the catastrophic consequences by occupant during an accident, the Malaysian government has reinforced the same regulation upon superstructure construction. This paper discusses collapse mechanism analysis of a superstructure vehicle using a Crash D nonlinear analysis computer program based on this regulation. The analysis starts by hand calculation to define the required energy absorption by the chosen structure. Simple calculations were then performed to define the weakest collapse mechanism after undesirable collapse modes are eliminated. There are few factors highlighted in this work to pass the regulation. Using the selected cross section, Crash D simulation showed a good result. Generally, the deformation is linearly correlates to the energy absorption for the structure with low stiffness. Failure of critical members such as vertical lower side wall must be avoided to sustain safety of the passenger compartment and prevent from severe and fatal injuries to the trapped occupant.
PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction
Bao, Forrest Sheng; Liu, Xin; Zhang, Christina
Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting ...
Distortion Analysis Toolkit—A Software Tool for Easy Analysis of Nonlinear Audio Systems
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Jyri Pakarinen
Full Text Available Several audio effects devices deliberately add nonlinear distortion to the processed signal in order to create a desired sound. When creating virtual analog models of nonlinearly distorting devices, it would be very useful to carefully analyze the type of distortion, so that the model could be made as realistic as possible. While traditional system analysis tools such as the frequency response give detailed information on the operation of linear and time-invariant systems, they are less useful for analyzing nonlinear devices. Furthermore, although there do exist separate algorithms for nonlinear distortion analysis, there is currently no unified, easy-to-use tool for rapid analysis of distorting audio systems. This paper offers a remedy by introducing a new software tool for easy analysis of distorting effects. A comparison between a well-known guitar tube amplifier and two commercial software simulations is presented as a case study. This freely available software is written in Matlab language, but the analysis tool can also run as a standalone program, so the user does not need to have Matlab installed in order to perform the analysis.
Physics, Nonlinear Time Series Analysis, Data Assimilation and Hyperfast Modeling of Nonlinear Ocean Waves
Hyperfast Modeling of Nonlinear Ocean Waves A. R. Osborne Dipartimento di Fisica Generale, Università di Torino Via Pietro Giuria 1, 10125...PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Universit?i Torino,Dipartimento di Fisica Generale,Via Pietro Giuria 1,10125 Torino, Italy, 8. PERFORMING
Evaluation of time integration methods for transient response analysis of nonlinear structures
International Nuclear Information System (INIS)
Park, K.C.
Recent developments in the evaluation of direct time integration methods for the transient response analysis of nonlinear structures are presented. These developments, which are based on local stability considerations of an integrator, show that the interaction between temporal step size and nonlinearities of structural systems has a pronounced effect on both accuracy and stability of a given time integration method. The resulting evaluation technique is applied to a model nonlinear problem, in order to: 1) demonstrate that it eliminates the present costly process of evaluating time integrator for nonlinear structural systems via extensive numerical experiments; 2) identify the desirable characteristics of time integration methods for nonlinear structural problems; 3) develop improved stiffly-stable methods for application to nonlinear structures. Extension of the methodology for examination of the interaction between a time integrator and the approximate treatment of nonlinearities (such as due to pseudo-force or incremental solution procedures) is also discussed. (Auth.)
Askamp, Jessica; van Putten, Michel Johannes Antonius Maria
The sensitivity of routine EEG recordings for interictal epileptiform discharges in epilepsy is limited. In some patients, inpatient video-EEG may be performed to increase the likelihood of finding abnormalities. Although many agree that home EEG recordings may provide a cost-effective alternative
Correlation between detrended fluctuation analysis and the Lempel-Ziv complexity in nonlinear time series analysis
International Nuclear Information System (INIS)
Tang You-Fu; Liu Shu-Lin; Jiang Rui-Hong; Liu Ying-Hui
We study the correlation between detrended fluctuation analysis (DFA) and the Lempel-Ziv complexity (LZC) in nonlinear time series analysis in this paper. Typical dynamic systems including a logistic map and a Duffing model are investigated. Moreover, the influence of Gaussian random noise on both the DFA and LZC are analyzed. The results show a high correlation between the DFA and LZC, which can quantify the non-stationarity and the nonlinearity of the time series, respectively. With the enhancement of the random component, the exponent a and the normalized complexity index C show increasing trends. In addition, C is found to be more sensitive to the fluctuation in the nonlinear time series than α. Finally, the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve, and an effective fault diagnosis result is obtained
SPHARA--a generalized spatial Fourier analysis for multi-sensor systems with non-uniformly arranged sensors: application to EEG.
Graichen, Uwe; Eichardt, Roland; Fiedler, Patrique; Strohmeier, Daniel; Zanow, Frank; Haueisen, Jens
Important requirements for the analysis of multichannel EEG data are efficient techniques for signal enhancement, signal decomposition, feature extraction, and dimensionality reduction. We propose a new approach for spatial harmonic analysis (SPHARA) that extends the classical spatial Fourier analysis to EEG sensors positioned non-uniformly on the surface of the head. The proposed method is based on the eigenanalysis of the discrete Laplace-Beltrami operator defined on a triangular mesh. We present several ways to discretize the continuous Laplace-Beltrami operator and compare the properties of the resulting basis functions computed using these discretization methods. We apply SPHARA to somatosensory evoked potential data from eleven volunteers and demonstrate the ability of the method for spatial data decomposition, dimensionality reduction and noise suppression. When employing SPHARA for dimensionality reduction, a significantly more compact representation can be achieved using the FEM approach, compared to the other discretization methods. Using FEM, to recover 95% and 99% of the total energy of the EEG data, on average only 35% and 58% of the coefficients are necessary. The capability of SPHARA for noise suppression is shown using artificial data. We conclude that SPHARA can be used for spatial harmonic analysis of multi-sensor data at arbitrary positions and can be utilized in a variety of other applications.
Nonlinear regression analysis for evaluating tracer binding parameters using the programmable K1003 desk computer
International Nuclear Information System (INIS)
Sarrach, D.; Strohner, P.
The Gauss-Newton algorithm has been used to evaluate tracer binding parameters of RIA by nonlinear regression analysis. The calculations were carried out on the K1003 desk computer. Equations for simple binding models and its derivatives are presented. The advantages of nonlinear regression analysis over linear regression are demonstrated
Analysis of nonlinear systems with time varying inputs and its application to gain scheduling
Directory of Open Access Journals (Sweden)
J.-T. Lim
Full Text Available An analytical framework for analysis of a class of nonlinear systems with time varying inputs is presented. It is shown that the trajectories of the transformed nonlinear systems are uniformly bounded with an ultimate bound under certain conditions shown in this paper. The result obtained is useful for applications, in particular, analysis and design of gain scheduling.
Development of nonlinear dynamic analysis program for nuclear piping systems
International Nuclear Information System (INIS)
Kamichika, Ryoichi; Izawa, Masahiro; Yamadera, Masao
In the design for nuclear power piping, pipe-whip protection shall be considered in order to keep the function of safety related system even when postulated piping rupture occurs. This guideline was shown in U.S. Regulatory Guide 1.46 for the first time and has been applied in Japanese nuclear power plants. In order to analyze the dynamic behavior followed by pipe rupture, nonlinear analysis is required for the piping system including restraints which play the role of an energy absorber. REAPPS (Rupture Effective Analysis of Piping Systems) has been developed for this purpose. This program can be applied to general piping systems having branches etc. Pre- and post- processors are prepared in this program in order to easily input the data for the piping engineer and show the results optically by use of a graphic display respectively. The piping designer can easily solve many problems in his daily work by use of this program. This paper describes about the theoretical background and functions of this program and shows some examples. (author)
Qualitative analysis and traveling wave solutions for the perturbed nonlinear Schroedinger's equation with Kerr law nonlinearity
International Nuclear Information System (INIS)
Zhang Zaiyun; Liu Zhenhai; Miao Xiujin; Chen Yuezhong
In this Letter, we investigate the perturbed nonlinear Schroedinger's equation (NLSE) with Kerr law nonlinearity. All explicit expressions of the bounded traveling wave solutions for the equation are obtained by using the bifurcation method and qualitative theory of dynamical systems. These solutions contain bell-shaped solitary wave solutions, kink-shaped solitary wave solutions and Jacobi elliptic function periodic solutions. Moreover, we point out the region which these periodic wave solutions lie in. We present the relation between the bounded traveling wave solution and the energy level h. We find that these periodic wave solutions tend to the corresponding solitary wave solutions as h increases or decreases. Finally, for some special selections of the energy level h, it is shown that the exact periodic solutions evolute into solitary wave solution.
Multi-Variate EEG Analysis as a Novel Tool to Examine Brain Responses to Naturalistic Music Stimuli.
Directory of Open Access Journals (Sweden)
Irene Sturm
Full Text Available Note onsets in music are acoustic landmarks providing auditory cues that underlie the perception of more complex phenomena such as beat, rhythm, and meter. For naturalistic ongoing sounds a detailed view on the neural representation of onset structure is hard to obtain, since, typically, stimulus-related EEG signatures are derived by averaging a high number of identical stimulus presentations. Here, we propose a novel multivariate regression-based method extracting onset-related brain responses from the ongoing EEG. We analyse EEG recordings of nine subjects who passively listened to stimuli from various sound categories encompassing simple tone sequences, full-length romantic piano pieces and natural (non-music soundscapes. The regression approach reduces the 61-channel EEG to one time course optimally reflecting note onsets. The neural signatures derived by this procedure indeed resemble canonical onset-related ERPs, such as the N1-P2 complex. This EEG projection was then utilized to determine the Cortico-Acoustic Correlation (CACor, a measure of synchronization between EEG signal and stimulus. We demonstrate that a significant CACor (i can be detected in an individual listener's EEG of a single presentation of a full-length complex naturalistic music stimulus, and (ii it co-varies with the stimuli's average magnitudes of sharpness, spectral centroid, and rhythmic complexity. In particular, the subset of stimuli eliciting a strong CACor also produces strongly coordinated tension ratings obtained from an independent listener group in a separate behavioral experiment. Thus musical features that lead to a marked physiological reflection of tone onsets also contribute to perceived tension in music.
Quantitative topographic differentiation of the neonatal EEG.
Paul, Karel; Krajca, Vladimír; Roth, Zdenek; Melichar, Jan; Petránek, Svojmil
To test the discriminatory topographic potential of a new method of the automatic EEG analysis in neonates. A quantitative description of the neonatal EEG can contribute to the objective assessment of the functional state of the brain, and may improve the precision of diagnosing cerebral dysfunctions manifested by 'disorganization', 'dysrhythmia' or 'dysmaturity'. 21 healthy, full-term newborns were examined polygraphically during sleep (EEG-8 referential derivations, respiration, ECG, EOG, EMG). From each EEG record, two 5-min samples (one from the middle of quiet sleep, the other from the middle of active sleep) were subject to subsequent automatic analysis and were described by 13 variables: spectral features and features describing shape and variability of the signal. The data from individual infants were averaged and the number of variables was reduced by factor analysis. All factors identified by factor analysis were statistically significantly influenced by the location of derivation. A large number of statistically significant differences were also established when comparing the effects of individual derivations on each of the 13 measured variables. Both spectral features and features describing shape and variability of the signal are largely accountable for the topographic differentiation of the neonatal EEG. The presented method of the automatic EEG analysis is capable to assess the topographic characteristics of the neonatal EEG, and it is adequately sensitive and describes the neonatal electroencephalogram with sufficient precision. The discriminatory capability of the used method represents a promise for their application in the clinical practice.
Qualitative analysis of nonlinear power oscillation in NSRR
International Nuclear Information System (INIS)
Suzudo, T.; Shinohara, Y.
The performance of the automatic control system of NSRR is investigated experimentally and theoretically in connection with the power oscillation. A subsystem in the automatic control system relevant to the onset of the power oscillation is determined, and it is found that the subsystem possesses nonlinearity. Although the detailed mechanism of the nonlinearity cannot be identified because of lack of signals measured inside the subsystem, the input and output signals imply that the nonlinearity is a sort of backlash. A simplified reactor dynamic model with backlash simulates the dynamics of the NSRR power oscillation. (Author)
Mathematical Methods in Wave Propagation: Part 2--Non-Linear Wave Front Analysis
Jeffrey, Alan
The paper presents applications and methods of analysis for non-linear hyperbolic partial differential equations. The paper is concluded by an account of wave front analysis as applied to the piston problem of gas dynamics. (JG)
Simulation Analysis of Helicopter Ground Resonance Nonlinear Dynamics
Zhu, Yan; Lu, Yu-hui; Ling, Ai-min
In order to accurately predict the dynamic instability of helicopter ground resonance, a modeling and simulation method of helicopter ground resonance considering nonlinear dynamic characteristics of components (rotor lead-lag damper, landing gear wheel and absorber) is presented. The numerical integral method is used to calculate the transient responses of the body and rotor, simulating some disturbance. To obtain quantitative instabilities, Fast Fourier Transform (FFT) is conducted to estimate the modal frequencies, and the mobile rectangular window method is employed in the predictions of the modal damping in terms of the response time history. Simulation results show that ground resonance simulation test can exactly lead up the blade lead-lag regressing mode frequency, and the modal damping obtained according to attenuation curves are close to the test results. The simulation test results are in accordance with the actual accident situation, and prove the correctness of the simulation method. This analysis method used for ground resonance simulation test can give out the results according with real helicopter engineering tests.
Analysis of nonlinear optical properties in donor–acceptor materials
Energy Technology Data Exchange (ETDEWEB)
Day, Paul N. [Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio 45433 (United States); General Dynamics Information Technology, Inc., Dayton, Ohio 45431 (United States); Pachter, Ruth [Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio 45433 (United States); Nguyen, Kiet A. [Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio 45433 (United States); UES, Inc., Dayton, Ohio 45432 (United States)
Time-dependent density functional theory has been used to calculate nonlinear optical (NLO) properties, including the first and second hyperpolarizabilities as well as the two-photon absorption cross-section, for the donor-acceptor molecules p-nitroaniline and dimethylamino nitrostilbene, and for respective materials attached to a gold dimer. The CAMB3LYP, B3LYP, PBE0, and PBE exchange-correlation functionals all had fair but variable performance when compared to higher-level theory and to experiment. The CAMB3LYP functional had the best performance on these compounds of the functionals tested. However, our comprehensive analysis has shown that quantitative prediction of hyperpolarizabilities is still a challenge, hampered by inadequate functionals, basis sets, and solvation models, requiring further experimental characterization. Attachment of the Au{sub 2}S group to molecules already known for their relatively large NLO properties was found to further enhance the response. While our calculations show a modest enhancement for the first hyperpolarizability, the enhancement of the second hyperpolarizability is predicted to be more than an order of magnitude.
Non-linear analysis of solid propellant burning rate behavior
Energy Technology Data Exchange (ETDEWEB)
Junye Wang [Zhejiang Univ. of Technology, College of Mechanical and Electrical Engineering, Hanzhou (China)
The parametric analysis of the thermal wave model of the non-steady combustion of solid propellants is carried out under a sudden compression. First, to observe non-linear effects, solutions are obtained using a computer under prescribed pressure variations. Then, the effects of rearranging the spatial mesh, additional points, and the time step on numerical solutions are evaluated. Finally, the behaviour of the thermal wave combustion model is examined under large heat releases (H) and a dynamic factor ({beta}). The numerical predictions show that (1) the effect of a dynamic factor ({beta}), related to the magnitude of dp/dt, on the peak burning rate increases as the value of beta increases. However, unsteady burning rate 'runaway' does not appear and will return asymptotically to ap{sup n}, when {beta}{>=}10.0. The burning rate 'runaway' is a numerical difficulty, not a solution to the models. (2) At constant beta and m, the amplitude of the burning rate increases with increasing H. However, the increase in the burning rate amplitude is stepwise, and there is no apparent intrinsic instability limit. A damped oscillation of burning rate occurs when the value of H is less. However, when H>1.0, the state of an intrinsically unstable model is composed of repeated, amplitude spikes, i.e. an undamped oscillation occurs. (3) The effect of the time step on the peak burning rate increases as H increases. (Author)
Time-variant coherence between heart rate variability and EEG activity in epileptic patients: an advanced coupling analysis between physiological networks
International Nuclear Information System (INIS)
Piper, D; Schiecke, K; Pester, B; Witte, H; Benninger, F; Feucht, M
Time-variant coherence analysis between the heart rate variability (HRV) and the channel-related envelopes of adaptively selected EEG components was used as an indicator for the occurrence of (correlative) couplings between the central autonomic network (CAN) and the epileptic network before, during and after epileptic seizures. Two groups of patients were investigated, a group with left and a group with right hemispheric temporal lobe epilepsy. The individual EEG components were extracted by a signal-adaptive approach, the multivariate empirical mode decomposition, and the envelopes of each resulting intrinsic mode function (IMF) were computed by using Hilbert transform. Two IMFs, whose envelopes were strongly correlated with the HRV's low-frequency oscillation (HRV-LF; ≈0.1 Hz) before and after the seizure were identified. The frequency ranges of these IMFs correspond to the EEG delta-band. The time-variant coherence was statistically quantified and tensor decomposition of the time-frequency coherence maps was applied to explore the topography-time-frequency characteristics of the coherence analysis. Results allow the hypothesis that couplings between the CAN, which controls the cardiovascular-cardiorespiratory system, and the 'epileptic neural network' exist. Additionally, our results confirm the hypothesis of a right hemispheric lateralization of sympathetic cardiac control of the HRV-LF. (paper)
Improving the Specificity of EEG for Diagnosing Alzheimer's Disease
Directory of Open Access Journals (Sweden)
François-B. Vialatte
Full Text Available Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD. However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz, α1 (7.5–9.5 Hz, α2 (9.5–12.5 Hz, and β (12.5–25 Hz. The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.
Advanced Seismic Fragility Modeling using Nonlinear Soil-Structure Interaction Analysis
Energy Technology Data Exchange (ETDEWEB)
Bolisetti, Chandu [Idaho National Lab. (INL), Idaho Falls, ID (United States); Coleman, Justin [Idaho National Lab. (INL), Idaho Falls, ID (United States); Talaat, Mohamed [Simpson-Gupertz & Heger, Waltham, MA (United States); Hashimoto, Philip [Simpson-Gupertz & Heger, Waltham, MA (United States)
The goal of this effort is to compare the seismic fragilities of a nuclear power plant system obtained by a traditional seismic probabilistic risk assessment (SPRA) and an advanced SPRA that utilizes Nonlinear Soil-Structure Interaction (NLSSI) analysis. Soil-structure interaction (SSI) response analysis for a traditional SPRA involves the linear analysis, which ignores geometric nonlinearities (i.e., soil and structure are glued together and the soil material undergoes tension when the structure uplifts). The NLSSI analysis will consider geometric nonlinearities.
Semi-classical analysis for nonlinear Schrödinger equations
Carles, Remi
These lecture notes review recent results on the high-frequency analysis of nonlinear Schrödinger equations in the presence of an external potential. The book consists of two relatively independent parts: WKB analysis, and caustic crossing. In the first part, the basic linear WKB theory is constructed and then extended to the nonlinear framework. The most difficult supercritical case is discussed in detail, together with some of its consequences concerning instability phenomena. Applications of WKB analysis to functional analysis, in particular to the Cauchy problem for nonlinear Schrödinger e
Advances in dynamic relaxation techniques for nonlinear finite element analysis
International Nuclear Information System (INIS)
Sauve, R.G.; Metzger, D.R.
Traditionally, the finite element technique has been applied to static and steady-state problems using implicit methods. When nonlinearities exist, equilibrium iterations must be performed using Newton-Raphson or quasi-Newton techniques at each load level. In the presence of complex geometry, nonlinear material behavior, and large relative sliding of material interfaces, solutions using implicit methods often become intractable. A dynamic relaxation algorithm is developed for inclusion in finite element codes. The explicit nature of the method avoids large computer memory requirements and makes possible the solution of large-scale problems. The method described approaches the steady-state solution with no overshoot, a problem which has plagued researchers in the past. The method is included in a general nonlinear finite element code. A description of the method along with a number of new applications involving geometric and material nonlinearities are presented. They include: (1) nonlinear geometric cantilever plate; (2) moment-loaded nonlinear beam; and (3) creep of nuclear fuel channel assemblies
Nonlinear Analysis of the Space Shuttle Superlightweight External Fuel Tank
Nemeth, Michael P.; Britt, Vicki O.; Collins, Timothy J.; Starnes, James H., Jr.
Results of buckling and nonlinear analyses of the Space Shuttle external tank superlightweight liquid-oxygen (LO2) tank are presented. Modeling details and results are presented for two prelaunch loading conditions and for two full-scale structural tests that were conducted on the original external tank. The results illustrate three distinctly different types of nonlinear response for thin-walled shells subjected to combined mechanical and thermal loads. The nonlinear response phenomena consist of bifurcation-type buckling, short-wavelength nonlinear bending, and nonlinear collapse associated with a limit point. For each case, the results show that accurate predictions of non- linear behavior generally require a large-scale, high-fidelity finite-element model. Results are also presented that show that a fluid-filled launch-vehicle shell can be highly sensitive to initial geometric imperfections. In addition, results presented for two full-scale structural tests of the original standard-weight external tank suggest that the finite-element modeling approach used in the present study is sufficient for representing the nonlinear behavior of the superlightweight LO2 tank.
The solution of a coupled system of nonlinear physical problems using the homotopy analysis method
International Nuclear Information System (INIS)
El-Wakil, S A; Abdou, M A
In this article, the homotopy analysis method (HAM) has been applied to solve coupled nonlinear evolution equations in physics. The validity of this method has been successfully demonstrated by applying it to two nonlinear evolution equations, namely coupled nonlinear diffusion reaction equations and the (2+1)-dimensional Nizhnik-Novikov Veselov system. The results obtained by this method show good agreement with the ones obtained by other methods. The proposed method is a powerful and easy to use analytic tool for nonlinear problems and does not need small parameters in the equations. The HAM solutions contain an auxiliary parameter that provides a convenient way of controlling the convergence region of series solutions. The results obtained here reveal that the proposed method is very effective and simple for solving nonlinear evolution equations. The basic ideas of this approach can be widely employed to solve other strongly nonlinear problems.
Correntropy measures to detect daytime sleepiness from EEG signals
International Nuclear Information System (INIS)
Melia, Umberto; Vallverdú, Montserrat; Caminal, Pere; Guaita, Marc; Montserrat, Josep M; Vilaseca, Isabel; Salamero, Manel; Gaig, Carles; Santamaria, Joan
Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders and has a great impact on patients' lives. While many studies have been carried out in order to assess daytime sleepiness, automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on correntropy function analysis of EEG signals was proposed in order to detect patients suffering from EDS. Multichannel EEG signals were recorded during five Maintenance of Wakefulness Tests (MWT) and Multiple Sleep Latency Tests (MSLT) alternated throughout the day for patients suffering from sleep disordered breathing (SDB). A group of 20 patients with EDS was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60 s EEG windows in a waking state. Measures obtained from the cross-correntropy function (CCORR) and auto-correntropy function (ACORR) were calculated in the EEG frequency bands: δ, 0.1–4 Hz; θ, 4–8 Hz; α, 8–12 Hz; β, 12–30 Hz; total band TB, 0.1–45 Hz. These functions permitted the quantification of complex signal properties and the non-linear couplings between different areas of the scalp. Statistical differences between EDS and WDS groups were mainly found in the β band during MSLT events (p-value < 0.0001). The WDS group presented more complexity in the occipital zone than the EDS group, while a stronger nonlinear coupling between the occipital and frontal regions was detected in EDS patients than in the WDS group. At best, ACORR and CCORR measures yielded sensitivity and specificity above 80% and the area under ROC curve (AUC) was above 0.85 in classifying EDS and WDS patients. These performances represent an improvement with respect to classical EEG indices applied in the same database (sensitivity and specificity were never above 80% and AUC was under 0.75). (paper)
Analyzing Electroencephalogram Signal Using EEG Lab
Directory of Open Access Journals (Sweden)
Mukesh BHARDWAJ
Full Text Available The EEG is composed of electrical potentials arising from several sources. Each source (including separate neural clusters, blink artifact or pulse artifact forms a unique topography onto the scalp – 'scalp map'. Scalp map may be 2-D or 3-D.These maps are mixed according to the principle of linear superposition. Independent component analysis (ICA attempts to reverse the superposition by separating the EEG into mutually independent scalp maps, or components. MATLAB toolbox and graphic user interface, EEGLAB is used for processing EEG data of any number of channels. Wavelet toolbox has been used for 2-D signal analysis.
Nonlinear structural analysis methods and their application to elevated temperature design: A US perspective
International Nuclear Information System (INIS)
Dhalla, A.K.
Technological advances over the last two decades have been assimilated into the routine design of Liquid Metal Reactor (LMR) structural components operating at elevated temperatures. The mature elevated temperature design technology is based upon: (a) an extensive material data base, (b) recent advances in nonlinear computational methods, and (c) conservative design criteria based upon past successful and reliable operating experiences with petrochemical and nonnuclear power plants. This survey paper provides a US perspective on the role of nonlinear analysis methods used in the design of LMR plants. The simplified and detailed nonlinear analysis methods and the level of computational effort required to qualify structural components for safe and reliable long-term operation are discussed. The paper also illustrates how a detailed nonlinear analysis can be used to resolve technical licensing issues, to understand complex nonlinear structural behavior, to identify predominant failure modes, and to guide future experimental programs
Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG
DEFF Research Database (Denmark)
Mørup, Morten; Hansen, Lars Kai; Hermann, Cristoph S.
by the inter-trial phase coherence (ITPC) encompassing ANOVA analysis of differences between conditions and 5-way analysis of channel x frequency x time x subject x condition. A flow chart is presented on how to perform data exploration using the PARAFAC decomposition on multi-way arrays. This includes (A......) channel x frequency x time 3-way arrays of F test values from a repeated measures analysis of variance (ANOVA) between two stimulus conditions; (B) subject-specific 3-way analyses; and (C) an overall 5-way analysis of channel x frequency x time x subject x condition. The PARAFAC decompositions were able...... of the 3-way array of ANOVA F test values clearly showed the difference of regions of interest across modalities, while the 5-way analysis enabled visualization of both quantitative and qualitative differences. Consequently, PARAFAC is a promising data exploratory tool in the analysis of the wavelets...
Drug Treated Schizophrenia, Schizoaffective and Bipolar Disorder Patients Evaluated by qEEG Absolute Spectral Power and Mean Frequency Analysis.
Wix-Ramos, Richard; Moreno, Xiomara; Capote, Eduardo; González, Gilbert; Uribe, Ezequiel; Eblen-Zajjur, Antonio
Research of electroencephalograph (EEG) power spectrum and mean frequency has shown inconsistent results in patients with schizophrenic, schizoaffective and bipolar disorders during medication when compared to normal subjects thus; the characterization of these parameters is an important task. We applied quantitative EEG (qEEG) to investigate 38 control, 15 schizophrenic, 7 schizoaffective and 11 bipolar disorder subjects which remaine under the administration of psychotropic drugs (except control group). Absolute spectral power (ASP), mean frequency and hemispheric electrical asymmetry were measured by 19 derivation qEEG. Group mean values were compared with non parametrical Mann-Whitney test and spectral EEG maps with z-score method at p Schizoaffective patients received neuroleptic+benzodiazepine (71.4%) and for bipolar disorder patients neuroleptic+antiepileptic (81.8%). Schizophrenic (at all derivations except for Fp1, Fp2, F8 and T6) and schizoaffective (only at C3) show higher values of ASP (+57.7% and +86.1% respectively) compared to control group. ASP of bipolar disorder patients did not show differences against control group. The mean frequency was higher at Fp1 (+14.2%) and Fp2 (+17.4%) in bipolar disorder patients than control group, but no differences were found in frequencies between schizophrenic or schizoaffective patients against the control group. Majority of spectral differences were found at the left hemisphere in schizophrenic and schizoaffective but not in bipolar disorder subjects. The present report contributes to characterize quantitatively the qEEG in drug treated schizophrenic, schizoaffective or bipolar disorder patients.
Optimal Threshold Determination for Discriminating Driving Anger Intensity Based on EEG Wavelet Features and ROC Curve Analysis
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Ping Wan
Full Text Available Driving anger, called "road rage", has become increasingly common nowadays, affecting road safety. A few researches focused on how to identify driving anger, however, there is still a gap in driving anger grading, especially in real traffic environment, which is beneficial to take corresponding intervening measures according to different anger intensity. This study proposes a method for discriminating driving anger states with different intensity based on Electroencephalogram (EEG spectral features. First, thirty drivers were recruited to conduct on-road experiments on a busy route in Wuhan, China where anger could be inducted by various road events, e.g., vehicles weaving/cutting in line, jaywalking/cyclist crossing, traffic congestion and waiting red light if they want to complete the experiments ahead of basic time for extra paid. Subsequently, significance analysis was used to select relative energy spectrum of β band (β% and relative energy spectrum of θ band (θ% for discriminating the different driving anger states. Finally, according to receiver operating characteristic (ROC curve analysis, the optimal thresholds (best cut-off points of β% and θ% for identifying none anger state (i.e., neutral were determined to be 0.2183 ≤ θ% < 1, 0 < β% < 0.2586; low anger state is 0.1539 ≤ θ% < 0.2183, 0.2586 ≤ β% < 0.3269; moderate anger state is 0.1216 ≤ θ% < 0.1539, 0.3269 ≤ β% < 0.3674; high anger state is 0 < θ% < 0.1216, 0.3674 ≤ β% < 1. Moreover, the discrimination performances of verification indicate that, the overall accuracy (Acc of the optimal thresholds of β% for discriminating the four driving anger states is 80.21%, while 75.20% for that of θ%. The results can provide theoretical foundation for developing driving anger detection or warning devices based on the relevant optimal thresholds.
A comparative analysis of alternative approaches for quantifying nonlinear dynamics in cardiovascular system.
Chen, Yun; Yang, Hui
Heart rate variability (HRV) analysis has emerged as an important research topic to evaluate autonomic cardiac function. However, traditional time and frequency-domain analysis characterizes and quantify only linear and stationary phenomena. In the present investigation, we made a comparative analysis of three alternative approaches (i.e., wavelet multifractal analysis, Lyapunov exponents and multiscale entropy analysis) for quantifying nonlinear dynamics in heart rate time series. Note that these extracted nonlinear features provide information about nonlinear scaling behaviors and the complexity of cardiac systems. To evaluate the performance, we used 24-hour HRV recordings from 54 healthy subjects and 29 heart failure patients, available in PhysioNet. Three nonlinear methods are evaluated not only individually but also in combination using three classification algorithms, i.e., linear discriminate analysis, quadratic discriminate analysis and k-nearest neighbors. Experimental results show that three nonlinear methods capture nonlinear dynamics from different perspectives and the combined feature set achieves the best performance, i.e., sensitivity 97.7% and specificity 91.5%. Collectively, nonlinear HRV features are shown to have the promise to identify the disorders in autonomic cardiovascular function.
Nonlinear analysis and dynamic structure in the energy market
Aghababa, Hajar
This research assesses the dynamic structure of the energy sector of the aggregate economy in the context of nonlinear mechanisms. Earlier studies have focused mainly on the price of the energy products when detecting nonlinearities in time series data of the energy market, and there is little mention of the production side of the market. Moreover, there is a lack of exploration about the implication of high dimensionality and time aggregation when analyzing the market's fundamentals. This research will address these gaps by including the quantity side of the market in addition to the price and by systematically incorporating various frequencies for sample sizes in three essays. The goal of this research is to provide an inclusive and exhaustive examination of the dynamics in the energy markets. The first essay begins with the application of statistical techniques, and it incorporates the most well-known univariate tests for nonlinearity with distinct power functions over alternatives and tests different null hypotheses. It utilizes the daily spot price observations on five major products in the energy market. The results suggest that the time series daily spot prices of the energy products are highly nonlinear in their nature. They demonstrate apparent evidence of general nonlinear serial dependence in each individual series, as well as nonlinearity in the first, second, and third moments of the series. The second essay examines the underlying mechanism of crude oil production and identifies the nonlinear structure of the production market by utilizing various monthly time series observations of crude oil production: the U.S. field, Organization of the Petroleum Exporting Countries (OPEC), non-OPEC, and the world production of crude oil. The finding implies that the time series data of the U.S. field, OPEC, and the world production of crude oil exhibit deep nonlinearity in their structure and are generated by nonlinear mechanisms. However, the dynamics of the non
Earthquake analysis with nonlinear soil-structure interaction and nonlinear supports of components
International Nuclear Information System (INIS)
Hansson, V.
For the determination of the seismic response of a structure the soil-structure interaction in most cases is modelled by a mass-spring-damper-system. Normally design concepts for components and piping are based on linear calculations and stress limitations. A concept for a reactor building for the HTR 100 consisted of a relatively high structure compared with the dimensions of the foundation. The structure was comparatively deep embedded in the soil, so here the embedment influences significantly the soil-structure interaction. The assembly of reactor vessel, heat exchanger and circulators has a height of about 37 m. Supports are arranged at different levels. Due to temperature deformations of the vessel and of the support constructions small gaps at the supports may only be avoided by complicated constructions of the supports. Nonlinear analyses were performed for soil, building and component with all supports. The finite element analyses used time histories. In order to describe the radiation damping the hysteresis of the soil with 1 percent material damping was considered. Nonlinearities in the interface of soil and foundation and due to gaps and friction at the supports were taken into account. The stiffness of the support constructions influences reactions and accelerations to a high extent. Properly chosen stiffnesses of the support constructions lead to a behaviour similar to linear elastic behaviour. 13 figs
The nonlinear finite element analysis program NUCAS (NUclear Containment Analysis System) for reinforced concrete containment building
Energy Technology Data Exchange (ETDEWEB)
Lee, Sang Jin; Lee, Hong Pyo; Seo, Jeong Moon [Korea Atomic Energy Research Institute, Taejeon (Korea)
The maim goal of this research is to develop a nonlinear finite element analysis program NUCAS to accurately predict global and local failure modes of containment building subjected to internal pressure. In this report, we describe the techniques we developed throught this research. An adequate model to the analysis of containment building such as microscopic material model is adopted and it applied into the development Reissner-Mindlin degenerated shell element. To avoid finite element deficiencies, the substitute strains based on the assumed strain method is used in the shell formulation. Arc-length control method is also adopted to fully trace the peak load-displacement path due to crack formation. In addition, a benchmark test suite is developed to investigate the performance of NUCAS and proposed as the future benchmark tests for nonlinear analysis of reinforced concrete. Finally, the input format of NUCAS and the examples of input/output file are described. 39 refs., 65 figs., 8 tabs. (Author)
Nonlinear vibrations analysis of rotating drum-disk coupling structure
Chaofeng, Li; Boqing, Miao; Qiansheng, Tang; Chenyang, Xi; Bangchun, Wen
A dynamic model of a coupled rotating drum-disk system with elastic support is developed in this paper. By considering the effects of centrifugal and Coriolis forces as well as rotation-induced hoop stress, the governing differential equation of the drum-disk is derived by Donnell's shell theory. The nonlinear amplitude-frequency characteristics of coupled structure are studied. The results indicate that the natural characteristics of the coupling structure are sensitive to the supporting stiffness of the disk, and the sensitive range is affected by rotating speeds. The circumferential wave numbers can affect the characteristics of the drum-disk structure. If the circumferential wave number n = 1 , the vibration response of the drum keeps a stable value under an unbalanced load of the disk, there is no coupling effect if n ≠ 1 . Under the excitation, the nonlinear hardening characteristics of the forward traveling wave are more evident than that of the backward traveling wave. Moreover, because of the coupling effect of the drum and the disk, the supporting stiffness of the disk has certain effect on the nonlinear characteristics of the forward and backward traveling waves. In addition, small length-radius and thickness-radius ratios have a significant effect on the nonlinear characteristics of the coupled structure, which means nonlinear shell theory should be adopted to design rotating drum's parameter for its specific structural parameters.
Gap-metric-based robustness analysis of nonlinear systems with full and partial feedback linearisation
Al-Gburi, A.; Freeman, C. T.; French, M. C.
This paper uses gap metric analysis to derive robustness and performance margins for feedback linearising controllers. Distinct from previous robustness analysis, it incorporates the case of output unstructured uncertainties, and is shown to yield general stability conditions which can be applied to both stable and unstable plants. It then expands on existing feedback linearising control schemes by introducing a more general robust feedback linearising control design which classifies the system nonlinearity into stable and unstable components and cancels only the unstable plant nonlinearities. This is done in order to preserve the stabilising action of the inherently stabilising nonlinearities. Robustness and performance margins are derived for this control scheme, and are expressed in terms of bounds on the plant nonlinearities and the accuracy of the cancellation of the unstable plant nonlinearity by the controller. Case studies then confirm reduced conservatism compared with standard methods.
Nonlinear bias analysis and correction of microwave temperature sounder observations for FY-3C meteorological satellite
Hu, Taiyang; Lv, Rongchuan; Jin, Xu; Li, Hao; Chen, Wenxin
The nonlinear bias analysis and correction of receiving channels in Chinese FY-3C meteorological satellite Microwave Temperature Sounder (MWTS) is a key technology of data assimilation for satellite radiance data. The thermal-vacuum chamber calibration data acquired from the MWTS can be analyzed to evaluate the instrument performance, including radiometric temperature sensitivity, channel nonlinearity and calibration accuracy. Especially, the nonlinearity parameters due to imperfect square-law detectors will be calculated from calibration data and further used to correct the nonlinear bias contributions of microwave receiving channels. Based upon the operational principles and thermalvacuum chamber calibration procedures of MWTS, this paper mainly focuses on the nonlinear bias analysis and correction methods for improving the calibration accuracy of the important instrument onboard FY-3C meteorological satellite, from the perspective of theoretical and experimental studies. Furthermore, a series of original results are presented to demonstrate the feasibility and significance of the methods.
Experimental analysis of nonlinear oscillations in the undergraduate physics laboratory
International Nuclear Information System (INIS)
Moreno, R; Page, A; Riera, J; Hueso, J L
In this paper, we present a simple experiment to introduce the nonlinear behaviour of oscillating systems in the undergraduate physics laboratory. The transverse oscillations of a spring allow reproduction of three totally different scenarios: linear oscillations, nonlinear oscillations reducible to linear for small displacements, and intrinsically nonlinear oscillations. The chosen approach consists of measuring the displacements using video photogrammetry and computing the velocities and the accelerations by means of a numerical differentiation algorithm. In this way, one can directly check the differential equation of the motion without having to integrate it, or perform an experimental study of the potential energy in each of the analysed scenarios. This experiment allows first year students to reflect on the consequences and the limits of the linearity assumption for small displacements that is so often made in technical studies. (paper)
A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG
Chen, Xun; Liu, Aiping; Peng, Hu; Ward, Rabab K.
Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For pra...
PLANS; a finite element program for nonlinear analysis of structures. Volume 2: User's manual
Pifko, A.; Armen, H., Jr.; Levy, A.; Levine, H.
The PLANS system, rather than being one comprehensive computer program, is a collection of finite element programs used for the nonlinear analysis of structures. This collection of programs evolved and is based on the organizational philosophy in which classes of analyses are treated individually based on the physical problem class to be analyzed. Each of the independent finite element computer programs of PLANS, with an associated element library, can be individually loaded and used to solve the problem class of interest. A number of programs have been developed for material nonlinear behavior alone and for combined geometric and material nonlinear behavior. The usage, capabilities, and element libraries of the current programs include: (1) plastic analysis of built-up structures where bending and membrane effects are significant, (2) three dimensional elastic-plastic analysis, (3) plastic analysis of bodies of revolution, and (4) material and geometric nonlinear analysis of built-up structures.
Bridge Diagnosis by Using Nonlinear Independent Component Analysis and Displacement Analysis
Zheng, Juanqing; Yeh, Yichun; Ogai, Harutoshi
A daily diagnosis system for bridge monitoring and maintenance is developed based on wireless sensors, signal processing, structure analysis, and displacement analysis. The vibration acceleration data of a bridge are firstly collected through the wireless sensor network by exerting. Nonlinear independent component analysis (ICA) and spectral analysis are used to extract the vibration frequencies of the bridge. After that, through a band pass filter and Simpson's rule the vibration displacement is calculated and the vibration model is obtained to diagnose the bridge. Since linear ICA algorithms work efficiently only in linear mixing environments, a nonlinear ICA model, which is more complicated, is more practical for bridge diagnosis systems. In this paper, we firstly use the post nonlinear method to change the signal data, after that perform linear separation by FastICA, and calculate the vibration displacement of the bridge. The processed data can be used to understand phenomena like corrosion and crack, and evaluate the health condition of the bridge. We apply this system to Nakajima Bridge in Yahata, Kitakyushu, Japan.
Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer's Disease EEG
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Isabella Palamara
Full Text Available An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel data as a unique block within a multi-scale framework. The basic complexity measure is done by using Permutation Entropy, a methodology for time series processing based on ordinal analysis. Permutation Entropy is conceptually simple, structurally robust to noise and artifacts, computationally very fast, which is relevant for designing portable diagnostics. Since time series derived from biological systems show structures on multiple spatial-temporal scales, the proposed technique can be useful for other types of biomedical signal analysis. In this work, the possibility of distinguish among the brain states related to Alzheimer's disease patients and Mild Cognitive Impaired subjects from normal healthy elderly is checked on a real, although quite limited, experimental database.
Model-Based Analysis and Optimization of the Mapping of Cortical Sources in the Spontaneous Scalp EEG
Directory of Open Access Journals (Sweden)
Andrei V. Sazonov
Full Text Available The mapping of brain sources into the scalp electroencephalogram (EEG depends on volume conduction properties of the head and on an electrode montage involving a reference. Mathematically, this source mapping (SM is fully determined by an observation function (OF matrix. This paper analyses the OF-matrix for a generation model for the desynchronized spontaneous EEG. The model involves a four-shell spherical volume conductor containing dipolar sources that are mutually uncorrelated so as to reflect the desynchronized EEG. The reference is optimized in order to minimize the impact in the SM of the sources located distant from the electrodes. The resulting reference is called the localized reference (LR. The OF-matrix is analyzed in terms of the relative power contribution of the sources and the cross-channel correlation coefficient for five existing references as well as for the LR. It is found that the Hjorth Laplacian reference is a fair approximation of the LR, and thus is close to optimum for practical intents and purposes. The other references have a significantly poorer performance. Furthermore, the OF-matrix is analyzed for limits to the spatial resolution for the EEG. These are estimated to be around 2 cm.
Model-based analysis and optimization of the mapping of cortical sources in the spontaneous scalp EEG
Sazonov, A.; Bergmans, J.W.M.; Cluitmans, P.J.M.; Griep, P.A.M.; Arends, J.B.A.M.; Boon, P.A.J.M.
The mapping of brain sources into the scalp electroencephalogram (EEG) depends on volume conduction properties of the head and on an electrode montage involving a reference. Mathematically, this source mapping (SM) is fully determined by an observation function (OF) matrix. This paper analyses the
Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task
Directory of Open Access Journals (Sweden)
Noor Kamal Al-Qazzaz
Full Text Available We performed a comparative study to select the efficient mother wavelet (MWT basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM task recorded through electro-encephalography (EEG. Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20, Symlets (sym1–sym20, and Coiflets (coif1–coif5. Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using "sym9" across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions.
Characterization of sleep need dissipation using EEG based slow-wave activity analysis in two age groups
Garcia-Molina, G.; Baehr, K.; Steele, B.; Tsoneva, T.K.; Pfundtner, S.; Mahadevan, A.; Papas, N.; Riedner, B.; Tononi, G.; White, D.
Introduction: In the two-process model of sleep regulation, slow-wave activity (SWA, EEG power in the 0.5–4 Hz band) is a direct indicator of sleep need. SWA builds up during NREM sleep, declines before the onset of REM sleep, remains low during REM and the level of increase in successive NREM
Nonlinear analysis of doubly curved shells: An analytical approach
Indian Academy of Sciences (India)
Е10Ж. 0rЕs└1Ж. iY j. И Й0rs. iY j└1 З 0rs. iY jЗ1Кa2jX. Е11Ж. Nonlinearity in the governing equations of motion is due to the product of the dependent variables. The quadratic extrapolation technique is used for the linearization and a typical nonlinear function Gj can be expressed at any step j as,. Gj И Е0rЖJЕ0sЖJ. И. И.
Seismic testing and analysis of a prototypic nonlinear piping system
International Nuclear Information System (INIS)
Barta, D.A.; Anderson, M.J.; Severud, L.K.
A series of seismic tests and analyses of a nonlinear Fast Flux Test Facility (FFTF) prototypic piping system are described, and measured responses are compared with analytical predictions. The test loop was representative of a typical LMFBR insulated small bore piping system and it was supported from a rigid test frame by prototypic dead weight supports, mechanical snubbers and pipe clamps. Various piping support configurations were tested and analyzed to evaluate the effects of free play and other nonlinear stiffness characteristics on the piping system response
Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy - A comparison with scalp EEG monitoring.
Zibrandtsen, I C; Kidmose, P; Christensen, C B; Kjaer, T W
Ear-EEG is recording of electroencephalography from a small device in the ear. This is the first study to compare ictal and interictal abnormalities recorded with ear-EEG and simultaneous scalp-EEG in an epilepsy monitoring unit. We recorded and compared simultaneous ear-EEG and scalp-EEG from 15 patients with suspected temporal lobe epilepsy. EEGs were compared visually by independent neurophysiologists. Correlation and time-frequency analysis was used to quantify the similarity between ear and scalp electrodes. Spike-averages were used to assess similarity of interictal spikes. There were no differences in sensitivity or specificity for seizure detection. Mean correlation coefficient between ear-EEG and nearest scalp electrode was above 0.6 with a statistically significant decreasing trend with increasing distance away from the ear. Ictal morphology and frequency dynamics can be observed from visual inspection and time-frequency analysis. Spike averages derived from ear-EEG electrodes yield a recognizable spike appearance. Our results suggest that ear-EEG can reliably detect electroencephalographic patterns associated with focal temporal lobe seizures. Interictal spike morphology from sufficiently large temporal spike sources can be sampled using ear-EEG. Ear-EEG is likely to become an important tool in clinical epilepsy monitoring and diagnosis. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
Ardeshna, Nikesh I
Coma is defined as a state of extreme unresponsiveness, in which a person exhibits no voluntary movement or behavior even to painful stimuli. The utilization of EEG for patients in coma has increased dramatically over the last few years. In fact, many institutions have set protocols for continuous EEG (cEEG) monitoring for patients in coma due to potential causes such as subarachnoid hemorrhage or cardiac arrest. Consequently, EEG plays an important role in diagnosis, managenent, and in some cases even prognosis of coma patients.
Only low frequency event-related EEG activity is compromised in multiple sclerosis: insights from an independent component clustering analysis.
Directory of Open Access Journals (Sweden)
Hanni Kiiski
Full Text Available Cognitive impairment (CI, often examined with neuropsychological tests such as the Paced Auditory Serial Addition Test (PASAT, affects approximately 65% of multiple sclerosis (MS patients. The P3b event-related potential (ERP, evoked when an infrequent target stimulus is presented, indexes cognitive function and is typically compared across subjects' scalp electroencephalography (EEG data. However, the clustering of independent components (ICs is superior to scalp-based EEG methods because it can accommodate the spatiotemporal overlap inherent in scalp EEG data. Event-related spectral perturbations (ERSPs; event-related mean power spectral changes and inter-trial coherence (ITCs; event-related consistency of spectral phase reveal a more comprehensive overview of EEG activity. Ninety-five subjects (56 MS patients, 39 controls completed visual and auditory two-stimulus P3b event-related potential tasks and the PASAT. MS patients were also divided into CI and non-CI groups (n = 18 in each based on PASAT scores. Data were recorded from 128-scalp EEG channels and 4 IC clusters in the visual, and 5 IC clusters in the auditory, modality were identified. In general, MS patients had significantly reduced ERSP theta power versus controls, and a similar pattern was observed for CI vs. non-CI MS patients. The ITC measures were also significantly different in the theta band for some clusters. The finding that MS patients had reduced P3b task-related theta power in both modalities is a reflection of compromised connectivity, likely due to demyelination, that may have disrupted early processes essential to P3b generation, such as orientating and signal detection. However, for posterior sources, MS patients had a greater decrease in alpha power, normally associated with enhanced cognitive function, which may reflect a compensatory mechanism in response to the compromised early cognitive processing.
Nonlinear Stability Analysis of a Composite Girder Cable-Stayed Bridge with Three Pylons during Construction
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Xiaoguang Deng
Full Text Available Based on the nonlinear stability analysis method, the 3D nonlinear finite element model of a composite girder cable-stayed bridge with three pylons is established to research the effect of factors including geometric nonlinearity, material nonlinearity, static wind load, and unbalanced construction load on the structural stability during construction. Besides, the structural nonlinear stability in different construction schemes and the determination of temporary pier position are also studied. The nonlinear stability safety factors are calculated to demonstrate the rationality and safety of construction schemes. The results show that the nonlinear stability safety factors of this bridge during construction meet the design requirement and the minimum value occurs in the maximum double cantilever stage. Besides, the nonlinear stability of the structure in the side of edge-pylon meets the design requirement in the two construction schemes. Furthermore, the temporary pier can improve the structure stability, effectively, and the actual position is reasonable. In addition, the local buckling of steel girder occurs earlier than overall instability under load in some cable tension stages. Finally, static wind load and the unbalanced construction load should be considered in the stability analysis for the adverse impact.
Z-score linear discriminant analysis for EEG based brain-computer interfaces.
Directory of Open Access Journals (Sweden)
Rui Zhang
Full Text Available Linear discriminant analysis (LDA is one of the most popular classification algorithms for brain-computer interfaces (BCI. LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA, which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.
Assessment of non-linear analysis finite element program (NONSAP) for inelastic analysis
International Nuclear Information System (INIS)
Chang, T.Y.; Prachuktam, S.; Reich, M.
An assessment on a nonlinear structural analysis finite element program called NONSAP is given with respect to its inelastic analysis capability for pressure vessels and components. The assessment was made from the review of its theoretical basis and bench mark problem runs. It was found that NONSAP has only limited capability for inelastic analysis. However, the program was written flexible enough that it can be easily extended or modified to suit the user's need. Moreover, some of the numerical difficulties in using NONSAP are pointed out
Bayesian inversion analysis of nonlinear dynamics in surface heterogeneous reactions.
Omori, Toshiaki; Kuwatani, Tatsu; Okamoto, Atsushi; Hukushima, Koji
It is essential to extract nonlinear dynamics from time-series data as an inverse problem in natural sciences. We propose a Bayesian statistical framework for extracting nonlinear dynamics of surface heterogeneous reactions from sparse and noisy observable data. Surface heterogeneous reactions are chemical reactions with conjugation of multiple phases, and they have the intrinsic nonlinearity of their dynamics caused by the effect of surface-area between different phases. We adapt a belief propagation method and an expectation-maximization (EM) algorithm to partial observation problem, in order to simultaneously estimate the time course of hidden variables and the kinetic parameters underlying dynamics. The proposed belief propagation method is performed by using sequential Monte Carlo algorithm in order to estimate nonlinear dynamical system. Using our proposed method, we show that the rate constants of dissolution and precipitation reactions, which are typical examples of surface heterogeneous reactions, as well as the temporal changes of solid reactants and products, were successfully estimated only from the observable temporal changes in the concentration of the dissolved intermediate product.
Nonlinear chaos-dynamical approach to analysis of atmospheric ...
Indian Academy of Sciences (India)
false nearest neighbors, Lyapunov's exponents, surrogate data, nonlinear prediction ... Chaotic dynamics; time series of the 222Rn concentration; universal complex ... tems is due to a number of applications, including the ..... Computer Engineering. ... Ternovsky,Quantum Systems in Physics, Chemistry, and. Biology, pp.
Approaches to the Optimal Nonlinear Analysis of Microcalorimeter Pulses
Fowler, J. W.; Pappas, C. G.; Alpert, B. K.; Doriese, W. B.; O'Neil, G. C.; Ullom, J. N.; Swetz, D. S.
We consider how to analyze microcalorimeter pulses for quantities that are nonlinear in the data, while preserving the signal-to-noise advantages of linear optimal filtering. We successfully apply our chosen approach to compute the electrothermal feedback energy deficit (the "Joule energy") of a pulse, which has been proposed as a linear estimator of the deposited photon energy.
Three-dimensional analysis of nonlinear plasma oscillation
International Nuclear Information System (INIS)
Miano, G.
In an underdense plasma a large-amplitude plasma oscillation may be produced by the beating of two external and colinear electromagnetic waves with a frequency difference approximately equal to the plasma frequency - plasma beat wave (PBW) resonant mechanism. The plasma oscillations are driven by the ponderomotive force arising from the beating of the two imposed electromagnetic waves. In this paper two pump electromagnetic waves with arbitrary transverse profiles have been considered. The plasma is described by using the three dimensinal weakly relativistic fluid equations. The nonlinear plasma oscillation dynamics is studied by using the eulerian description, the averaging and the multiple time scale methods. Unlike the linear theory a strong cross field coupling between longitudinal ans transverse electric field components of the plasma oscillation comes out, resulting in a nonlinear phase change and energy transfer between the two components. Unlike the one-dimensional nonlinear theory, the nonlinear frequency shift is caused by relativistic effects as well as by convective effects and electromagnetic field generated from the three dimensional plasma oscillation. The large amplitude plasma oscillation dynamics produced by a bunched relativistic electron beam with arbitrary transverse profile - plasma wave field (PWF) - or by a high power single frequency short electromagnetic pulse with arbitrary transverse profile - electromagnetic plasma wake field (EPWF) - may be described by means of the present theory. (orig.)
Nonlinear analysis of a cross-coupled quadrature harmonic oscillator
DEFF Research Database (Denmark)
Djurhuus, Torsten; Krozer, Viktor; Vidkjær, Jens
The dynamic equations governing the cross-coupled quadrature harmonic oscillator are derived assuming quasi-sinusoidal operation. This allows for an investigation of the previously reported tradeoff between close-to-carrier phase noise and quadrature precision. The results explain how nonlinearity...
A Cumulant-based Analysis of Nonlinear Magnetospheric Dynamics
International Nuclear Information System (INIS)
Johnson, Jay R.; Wing, Simon
Understanding magnetospheric dynamics and predicting future behavior of the magnetosphere is of great practical interest because it could potentially help to avert catastrophic loss of power and communications. In order to build good predictive models it is necessary to understand the most critical nonlinear dependencies among observed plasma and electromagnetic field variables in the coupled solar wind/magnetosphere system. In this work, we apply a cumulant-based information dynamical measure to characterize the nonlinear dynamics underlying the time evolution of the Dst and Kp geomagnetic indices, given solar wind magnetic field and plasma input. We examine the underlying dynamics of the system, the temporal statistical dependencies, the degree of nonlinearity, and the rate of information loss. We find a significant solar cycle dependence in the underlying dynamics of the system with greater nonlinearity for solar minimum. The cumulant-based approach also has the advantage that it is reliable even in the case of small data sets and therefore it is possible to avoid the assumption of stationarity, which allows for a measure of predictability even when the underlying system dynamics may change character. Evaluations of several leading Kp prediction models indicate that their performances are sub-optimal during active times. We discuss possible improvements of these models based on this nonparametric approach
Numerical bifurcation analysis of a class of nonlinear renewal equations
Breda, Dimitri; Diekmann, Odo; Liessi, Davide; Scarabel, Francesca
We show, by way of an example, that numerical bifurcation tools for ODE yield reliable bifurcation diagrams when applied to the pseudospectral approximation of a one-parameter family of nonlinear renewal equations. The example resembles logistic-and Ricker-type population equations and exhibits
Nonlinear analysis of a reactor building for airplane impact loadings
International Nuclear Information System (INIS)
Zimmermann, T.; Rodriguez, C.; Rebora, B.
The purpose is to analyze the influence of material nonlinear behavior on the response of a reinforced concrete reactor building and on equipment response for airplane impact loadings. Two analyses are performed: first, the impact of a slow-flying commercial airplane (Boeing 707), then the impact of a fast flying military airplane (Phantom). (orig./HP)
Identification of Nonlinear Oscillatory Activity Embedded in Broadband Neural Signals
Czech Academy of Sciences Publication Activity Database
Vejmelka, Martin; Paluš, Milan; Šušmáková, K.
Roč. 20, č. 2 (2010), s. 117-128 ISSN 0129-0657 R&D Projects: GA MŠk 7E08027 EU Projects: European Commission(XE) 200728 - BRAINSYNC Institutional research plan: CEZ:AV0Z10300504 Keywords : nonlinear dynamical systems * oscillations * random processes * time series analysis * EEG Subject RIV: FH - Neurology Impact factor: 4.237, year: 2010
Symmetries and semi-invariants in the analysis of nonlinear systems with control applications
Menini, Laura
This volume details the analysis of continuous- and discrete-time dynamical systems described by differential and difference equations. The theory is developed for general nonlinear systems and specialized for the class of Hamiltonian systems.
A Study on the Analysis and Optimal Control of Nonlinear Systems via Walsh Function
Energy Technology Data Exchange (ETDEWEB)
Kim, Jin Tae; Kim, Tai Hoon; Ahn, Doo Soo [Sungkyunkwan University (Korea); Lee, Myung Kyu [Kyungsung University (Korea)
This paper presents the new adaptive optimal scheme for the nonlinear systems, which is based on the Picard's iterative approximation and fast Walsh transform. It is well known that the Walsh function approach method is very difficult to apply for the analysis and optimal control of nonlinear systems. However, these problems can be easily solved by the improvement of the previous adaptive optimal scheme. The proposes method is easily applicable to the analysis and optimal control of nonlinear systems. (author). 15 refs., 6 figs., 1 tab.
Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability
Miller, Christopher B.; Bartlett, Delwyn J.; Mullins, Anna E.; Dodds, Kirsty L.; Gordon, Christopher J.; Kyle, Simon D.; Kim, Jong Won; D'Rozario, Angela L.; Lee, Rico S.C.; Comas, Maria; Marshall, Nathaniel S.; Yee, Brendon J.; Espie, Colin A.; Grunstein, Ronald R.
Study Objectives: To empirically derive and evaluate potential clusters of Insomnia Disorder through cluster analysis from polysomnography (PSG). We hypothesized that clusters would differ on neurocognitive performance, sleep-onset measures of quantitative (q)-EEG and heart rate variability (HRV). Methods: Research volunteers with Insomnia Disorder (DSM-5) completed a neurocognitive assessment and overnight PSG measures of total sleep time (TST), wake time after sleep onset (WASO), and sleep onset latency (SOL) were used to determine clusters. Results: From 96 volunteers with Insomnia Disorder, cluster analysis derived at least two clusters from objective sleep parameters: Insomnia with normal objective sleep duration (I-NSD: n = 53) and Insomnia with short sleep duration (I-SSD: n = 43). At sleep onset, differences in HRV between I-NSD and I-SSD clusters suggest attenuated parasympathetic activity in I-SSD (P insomnia clusters derived from cluster analysis differ in sleep onset HRV. Preliminary data suggest evidence for three clusters in insomnia with differences for sustained attention and sleep-onset q-EEG. Clinical Trial Registration: Insomnia 100 sleep study: Australia New Zealand Clinical Trials Registry (ANZCTR) identification number 12612000049875. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347742. Citation: Miller CB, Bartlett DJ, Mullins AE, Dodds KL, Gordon CJ, Kyle SD, Kim JW, D'Rozario AL, Lee RS, Comas M, Marshall NS, Yee BJ, Espie CA, Grunstein RR. Clusters of Insomnia Disorder: an exploratory cluster analysis of objective sleep parameters reveals differences in neurocognitive functioning, quantitative EEG, and heart rate variability. SLEEP 2016;39(11):1993–2004. PMID:27568796
EURDYN, Nonlinear Transient Analysis of Structure with Dynamic Loads
International Nuclear Information System (INIS)
Donea, J.; Giuliani, S.; Halleux, J.P.
1 - Description of program or function: The EURDYN computer codes are under development at JRC-Ispra since 1973 for the simulation of non- linear dynamic response of fast-reactor components submitted to impulsive loading due to abnormal working conditions. They are thus mainly used in reactor safety analysis but can apply to other fields. Indeed the codes compute the elasto-plastic transient response of 2-D and thin 3-D structures submitted to fast dynamic loading generated by explosions, impacts... and represented by time dependent pressures, concentrated loads and prescribed displacements, or by initial speeds. Two releases of the structural computer codes EURDYN 01 (2-D beams and triangles and axisymmetric conical shells and triangular tori), 02 (axisymmetric and 2-D quadratic iso-parametric elements) and 03 (triangular plate elements) have already been produced in 1976(1) and 1980(2). They include material (elasto-plasticity using the classical flow theory approach) and geometrical (large displacements and rotations treated by a co-rotational technique) nonlinearities. The present version (Release 3) has been completed mid-1982 and is documented in EUR 8357 EN. The new features of Release 3, as compared to the former ones, roughly consist in: - full large strain capability for 9-node iso-parametric elements (EURDYN 02), - generalized array dimensions, - introduction of the radial return algorithm for elasto-plastic material modelling, - extension of the energy check facility to the case of prescribed displacements, - possible interface to a post-processing package including time plot facilities (TPLOT). The theoretical aspects can be found in refs. 2,4,5,6,7,8. 2 - Method of solution: - Finite element space discretization. - Explicit time integration. - Lumped masses. - EURDYN 01: 2-D co-rotational formulation including constant strain triangles (plane or axisymmetric), beams and conical shells, this last element being particularly useful for the study of thin
Time-resolved analysis of nonlinear optical limiting for laser synthesized carbon nanoparticles
Chen, G. X.; Hong, M. H.
Nonlinear optical limiting materials have attracted much research interest in recent years. Carbon nanoparticles suspended in liquids show a strong nonlinear optical limiting function. It is important to investigate the nonlinear optical limiting process of carbon nanoparticles for further improving their nonlinear optical limiting performance. In this study, carbon nanoparticles were prepared by laser ablation of a carbon target in tetrahydrofuran (THF). Optical limiting properties of the samples were studied with 532-nm laser light, which is in the most sensitive wavelength band for human eyes. The shape of the laser pulse plays an important role for initializing the nonlinear optical limiting effect. Time-resolved analysis of laser pulses discovered 3 fluence stages of optical limiting. Theoretical simulation indicates that the optical limiting is initialized by a near-field optical enhancement effect.
Nonlinear FE analysis of reinforced concrete panels subjected to in-plane force
International Nuclear Information System (INIS)
Lee, H. P.; Lee, S. J.; Jun, Y. S.; Su, J. M.
Reinforced concrete structures subjected to in-plane force exhibit strong nonlinear behaviour due to complex material properties, cracks, interactions between concrete and steel and shear transfer exists in crack surface. Especially if there is crack formations, nonlinear behaviour increases. Thus the prediction of nonlinear behaviour of reinforced concrete includes failure or crushing is very difficult task. Various constitutive equations for concrete stress-strain relationship to predict nonlinear behaviour of reinforced concrete have been proposed. But the study for reinforced concrete analysis model using plastic material model is still demanded. So the purpose of this research is to formulate standard 8-node shell element using plasticity material model for concrete and to analyze nonlinear behaviour of RC panel subjected to in-plane force
Analysis of the possibilities of using EEG in assessing pilots' psychophysical condition
Directory of Open Access Journals (Sweden)
Marta GALANT
Full Text Available An excessive load on an operator's cognitive system can cause deterioration in perceptual abilities, decreased reaction time and increased probability of making an incorrect decision, which in turn can lead to a dangerous situation. Researching the cognitive load of an operator can therefore contribute to safer transportation. While there are many methods used in the study of cognitive load, they can be classified as either subjective assessments or objective assessments. This paper presents an analysis of the possibilities of using electroencephalography in assessing the psychophysical condition of the pilot. The investigation was conducted in the Simulation Research Laboratory in the Institute of Combustion Engines and Transport at Poznan University of Technology.
Analysis of correlation between white matter changes and functional responses in thalamic stroke: a DTI & EEG study.
Duru, Adil Deniz; Duru, Dilek Göksel; Yumerhodzha, Sami; Bebek, Nerses
Diffusion tensor imaging (DTI) allows in vivo structural brain mapping and detection of microstructural disruption of white matter (WM). One of the commonly used parameters for grading the anisotropic diffusivity in WM is fractional anisotropy (FA). FA value helps to quantify the directionality of the local tract bundle. Therefore, FA images are being used in voxelwise statistical analyses (VSA). The present study used Tract-Based Spatial Statistics (TBSS) of FA images across subjects, and computes the mean skeleton map to detect voxelwise knowledge of the tracts yielding to groupwise comparison. The skeleton image illustrates WM structure and shows any changes caused by brain damage. The microstructure of WM in thalamic stroke is investigated, and the VSA results of healthy control and thalamic stroke patients are reported. It has been shown that several skeleton regions were affected subject to the presence of thalamic stroke (FWE, p EEG (qEEG) scores and neurophysiological tests with the FA skeleton for the entire test group is also investigated. We compared measurements that are related to the same fibers across subjects, and discussed implications for VSA of WM in thalamic stroke cases, for the relationship between behavioral tests and FA skeletons, and for the correlation between the FA maps and qEEG scores.Results obtained through the regression analyses did not exceed the corrected statistical threshold values for multiple comparisons (uncorrected, p EEG, cingulum bundle and corpus callosum were found to be related. These areas are parts of the Default Mode Network (DMN) where DMN is known to be involved in resting state EEG theta activity. The relation between the EEG alpha band power values and FA values of the skeleton was found to support the cortico-thalamocortical cycles for both subject groups. Further, the neurophysiological tests including Benton Face Recognition (BFR), Digit Span test (DST), Warrington Topographic Memory test (WTMT
Multi-scale analysis of teleconnection indices: climate noise and nonlinear trend analysis
Directory of Open Access Journals (Sweden)
C. Franzke
Full Text Available The multi-scale nature and climate noise properties of teleconnection indices are examined by using the Empirical Mode Decomposition (EMD procedure. The EMD procedure allows for the analysis of non-stationary time series to extract physically meaningful intrinsic mode functions (IMF and nonlinear trends. The climatologically relevant monthly mean teleconnection indices of the North Atlantic Oscillation (NAO, the North Pacific index (NP and the Southern Annular Mode (SAM are analyzed.
The significance of IMFs and trends are tested against the null hypothesis of climate noise. The analysis of surrogate monthly mean time series from a red noise process shows that the EMD procedure is effectively a dyadic filter bank and the IMFs (except the first IMF are nearly Gaussian distributed. The distribution of the variance contained in IMFs of an ensemble of AR(1 simulations is nearly χ2 distributed. To test the statistical significance of the IMFs of the teleconnection indices and their nonlinear trends we utilize an ensemble of corresponding monthly averaged AR(1 processes, which we refer to as climate noise. Our results indicate that most of the interannual and decadal variability of the analysed teleconnection indices cannot be distinguished from climate noise. The NP and SAM indices have significant nonlinear trends, while the NAO has no significant trend when tested against a climate noise hypothesis.
Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks.
Li, Xiaowei; Hu, Bin; Shen, Ji; Xu, Tingting; Retcliffe, Martyn
Depression is a common mental disorder with growing prevalence; however current diagnoses of depression face the problem of patient denial, clinical experience and subjective biases from self-report. By using a combination of linear and nonlinear EEG features in our research, we aim to develop a more accurate and objective approach to depression detection that supports the process of diagnosis and assists the monitoring of risk factors. By classifying EEG features during free viewing task, an accuracy of 99.1%, which is the highest to our knowledge by far, was achieved using kNN classifier to discriminate depressed and non-depressed subjects. Furthermore, through correlation analysis, comparisons of performance on each electrode were discussed on the availability of single channel EEG recording depression detection system. Combined with wearable EEG collecting devices, our method offers the possibility of cost effective wearable ubiquitous system for doctors to monitor their patients with depression, and for normal people to understand their mental states in time.
LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.
Zhang, Tao; Chen, Wanzhong
Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.
Nonlinear dynamic mechanism of vocal tremor from voice analysis and model simulations
Zhang, Yu; Jiang, Jack J.
Nonlinear dynamic analysis and model simulations are used to study the nonlinear dynamic characteristics of vocal folds with vocal tremor, which can typically be characterized by low-frequency modulation and aperiodicity. Tremor voices from patients with disorders such as paresis, Parkinson's disease, hyperfunction, and adductor spasmodic dysphonia show low-dimensional characteristics, differing from random noise. Correlation dimension analysis statistically distinguishes tremor voices from normal voices. Furthermore, a nonlinear tremor model is proposed to study the vibrations of the vocal folds with vocal tremor. Fractal dimensions and positive Lyapunov exponents demonstrate the evidence of chaos in the tremor model, where amplitude and frequency play important roles in governing vocal fold dynamics. Nonlinear dynamic voice analysis and vocal fold modeling may provide a useful set of tools for understanding the dynamic mechanism of vocal tremor in patients with laryngeal diseases.
Lopes da Silva, F.; Mulert, C.; Lemieux, L.
The existence of the electrical activity of the brain (i.e. the electroencephalogram or EEG) was discovered more than a century ago by Caton. After the demonstration that the EEG could be recorded from the human scalp by Berger in the 1920s, it made a slow start before it became accepted as a method
Automated computation of autonomous spectral submanifolds for nonlinear modal analysis
Ponsioen, Sten; Pedergnana, Tiemo; Haller, George
We discuss an automated computational methodology for computing two-dimensional spectral submanifolds (SSMs) in autonomous nonlinear mechanical systems of arbitrary degrees of freedom. In our algorithm, SSMs, the smoothest nonlinear continuations of modal subspaces of the linearized system, are constructed up to arbitrary orders of accuracy, using the parameterization method. An advantage of this approach is that the construction of the SSMs does not break down when the SSM folds over its underlying spectral subspace. A further advantage is an automated a posteriori error estimation feature that enables a systematic increase in the orders of the SSM computation until the required accuracy is reached. We find that the present algorithm provides a major speed-up, relative to numerical continuation methods, in the computation of backbone curves, especially in higher-dimensional problems. We illustrate the accuracy and speed of the automated SSM algorithm on lower- and higher-dimensional mechanical systems.
Nonlinear analysis of composite thin-walled helicopter blades
Kalfon, J. P.; Rand, O.
Nonlinear theoretical modeling of laminated thin-walled composite helicopter rotor blades is presented. The derivation is based on nonlinear geometry with a detailed treatment of the body loads in the axial direction which are induced by the rotation. While the in-plane warping is neglected, a three-dimensional generic out-of-plane warping distribution is included. The formulation may also handle varying thicknesses and mass distribution along the cross-sectional walls. The problem is solved by successive iterations in which a system of equations is constructed and solved for each cross-section. In this method, the differential equations in the spanwise directions are formulated and solved using a finite-differences scheme which allows simple adaptation of the spanwise discretization mesh during iterations.
A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal.
Nayak, Suraj K; Bit, Arindam; Dey, Anilesh; Mohapatra, Biswajit; Pal, Kunal
Electrocardiogram (ECG) signal analysis has received special attention of the researchers in the recent past because of its ability to divulge crucial information about the electrophysiology of the heart and the autonomic nervous system activity in a noninvasive manner. Analysis of the ECG signals has been explored using both linear and nonlinear methods. However, the nonlinear methods of ECG signal analysis are gaining popularity because of their robustness in feature extraction and classification. The current study presents a review of the nonlinear signal analysis methods, namely, reconstructed phase space analysis, Lyapunov exponents, correlation dimension, detrended fluctuation analysis (DFA), recurrence plot, Poincaré plot, approximate entropy, and sample entropy along with their recent applications in the ECG signal analysis.
A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal
Mohapatra, Biswajit
Electrocardiogram (ECG) signal analysis has received special attention of the researchers in the recent past because of its ability to divulge crucial information about the electrophysiology of the heart and the autonomic nervous system activity in a noninvasive manner. Analysis of the ECG signals has been explored using both linear and nonlinear methods. However, the nonlinear methods of ECG signal analysis are gaining popularity because of their robustness in feature extraction and classification. The current study presents a review of the nonlinear signal analysis methods, namely, reconstructed phase space analysis, Lyapunov exponents, correlation dimension, detrended fluctuation analysis (DFA), recurrence plot, Poincaré plot, approximate entropy, and sample entropy along with their recent applications in the ECG signal analysis. PMID:29854361
Nonlinear analysis of reinforced concrete structures using software package abaqus
Marković Nemanja; Stojić Dragoslav; Cvetković Radovan
Reinforced concrete (AB) is characterized by huge inhomogeneity resulting from the material characteristics of the concrete, then, quasi-brittle behavior during failure. These and other phenomena require the introduction of material nonlinearity in the modeling of reinforced concrete structures. This paper presents the modeling reinforced concrete in the software package ABAQUS. A brief theoretical overview is presented of methods such as: Concrete Damage Plasticity (CDP), Smeared Concrete Cr...
Nonlinear thermal reduced model for Microwave Circuit Analysis
Chang, Christophe; Sommet, Raphael; Quéré, Raymond; Dueme, Ph.
With the constant increase of transistor power density, electro thermal modeling is becoming a necessity for accurate prediction of device electrical performances. For this reason, this paper deals with a methodology to obtain a precise nonlinear thermal model based on Model Order Reduction of a three dimensional thermal Finite Element (FE) description. This reduced thermal model is based on the Ritz vector approach which ensure the steady state solution in every case. An equi...
Multiscale analysis of nonlinear systems using computational homology
Energy Technology Data Exchange (ETDEWEB)
Konstantin Mischaikow; Michael Schatz; William Kalies; Thomas Wanner
This is a collaborative project between the principal investigators. However, as is to be expected, different PIs have greater focus on different aspects of the project. This report lists these major directions of research which were pursued during the funding period: (1) Computational Homology in Fluids - For the computational homology effort in thermal convection, the focus of the work during the first two years of the funding period included: (1) A clear demonstration that homology can sensitively detect the presence or absence of an important flow symmetry, (2) An investigation of homology as a probe for flow dynamics, and (3) The construction of a new convection apparatus for probing the effects of large-aspect-ratio. (2) Computational Homology in Cardiac Dynamics - We have initiated an effort to test the use of homology in characterizing data from both laboratory experiments and numerical simulations of arrhythmia in the heart. Recently, the use of high speed, high sensitivity digital imaging in conjunction with voltage sensitive fluorescent dyes has enabled researchers to visualize electrical activity on the surface of cardiac tissue, both in vitro and in vivo. (3) Magnetohydrodynamics - A new research direction is to use computational homology to analyze results of large scale simulations of 2D turbulence in the presence of magnetic fields. Such simulations are relevant to the dynamics of black hole accretion disks. The complex flow patterns from simulations exhibit strong qualitative changes as a function of magnetic field strength. Efforts to characterize the pattern changes using Fourier methods and wavelet analysis have been unsuccessful. (4) Granular Flow - two experts in the area of granular media are studying 2D model experiments of earthquake dynamics where the stress fields can be measured; these stress fields from complex patterns of 'force chains' that may be amenable to analysis using computational homology. (5) Microstructure
Multiscale analysis of nonlinear systems using computational homology
Energy Technology Data Exchange (ETDEWEB)
Konstantin Mischaikow, Rutgers University/Georgia Institute of Technology, Michael Schatz, Georgia Institute of Technology, William Kalies, Florida Atlantic University, Thomas Wanner,George Mason University
This is a collaborative project between the principal investigators. However, as is to be expected, different PIs have greater focus on different aspects of the project. This report lists these major directions of research which were pursued during the funding period: (1) Computational Homology in Fluids - For the computational homology effort in thermal convection, the focus of the work during the first two years of the funding period included: (1) A clear demonstration that homology can sensitively detect the presence or absence of an important flow symmetry, (2) An investigation of homology as a probe for flow dynamics, and (3) The construction of a new convection apparatus for probing the effects of large-aspect-ratio. (2) Computational Homology in Cardiac Dynamics - We have initiated an effort to test the use of homology in characterizing data from both laboratory experiments and numerical simulations of arrhythmia in the heart. Recently, the use of high speed, high sensitivity digital imaging in conjunction with voltage sensitive fluorescent dyes has enabled researchers to visualize electrical activity on the surface of cardiac tissue, both in vitro and in vivo. (3) Magnetohydrodynamics - A new research direction is to use computational homology to analyze results of large scale simulations of 2D turbulence in the presence of magnetic fields. Such simulations are relevant to the dynamics of black hole accretion disks. The complex flow patterns from simulations exhibit strong qualitative changes as a function of magnetic field strength. Efforts to characterize the pattern changes using Fourier methods and wavelet analysis have been unsuccessful. (4) Granular Flow - two experts in the area of granular media are studying 2D model experiments of earthquake dynamics where the stress fields can be measured; these stress fields from complex patterns of 'force chains' that may be amenable to analysis using computational homology. (5) Microstructure
Nonlinear Analysis and Intelligent Control of Integrated Vehicle Dynamics
Directory of Open Access Journals (Sweden)
C. Huang
Full Text Available With increasing and more stringent requirements for advanced vehicle integration, including vehicle dynamics and control, traditional control and optimization strategies may not qualify for many applications. This is because, among other factors, they do not consider the nonlinear characteristics of practical systems. Moreover, the vehicle wheel model has some inadequacies regarding the sideslip angle, road adhesion coefficient, vertical load, and velocity. In this paper, an adaptive neural wheel network is introduced, and the interaction between the lateral and vertical dynamics of the vehicle is analyzed. By means of nonlinear analyses such as the use of a bifurcation diagram and the Lyapunov exponent, the vehicle is shown to exhibit complicated motions with increasing forward speed. Furthermore, electric power steering (EPS and active suspension system (ASS, which are based on intelligent control, are used to reduce the nonlinear effect, and a negotiation algorithm is designed to manage the interdependences and conflicts among handling stability, driving smoothness, and safety. Further, a rapid control prototype was built using the hardware-in-the-loop simulation platform dSPACE and used to conduct a real vehicle test. The results of the test were consistent with those of the simulation, thereby validating the proposed control.
The analysis of the influence of fractal structure of stimuli on fractal dynamics in fixational eye movements and EEG signal
Namazi, Hamidreza; Kulish, Vladimir V.; Akrami, Amin
One of the major challenges in vision research is to analyze the effect of visual stimuli on human vision. However, no relationship has been yet discovered between the structure of the visual stimulus, and the structure of fixational eye movements. This study reveals the plasticity of human fixational eye movements in relation to the 'complex' visual stimulus. We demonstrated that the fractal temporal structure of visual dynamics shifts towards the fractal dynamics of the visual stimulus (image). The results showed that images with higher complexity (higher fractality) cause fixational eye movements with lower fractality. Considering the brain, as the main part of nervous system that is engaged in eye movements, we analyzed the governed Electroencephalogram (EEG) signal during fixation. We have found out that there is a coupling between fractality of image, EEG and fixational eye movements. The capability observed in this research can be further investigated and applied for treatment of different vision disorders.
Studentized Continuous Wavelet Transform (t-CWT in the Analysis of Individual ERPs: Real and Simulated EEG Data
Directory of Open Access Journals (Sweden)
Ruben Gustav Leonhardt Real
Full Text Available This study aimed at evaluating the performance of the Studentized Continuous Wavelet Transform (t-CWT as a method for the extraction and assessment of event-related brain potentials (ERP in data from a single subject. Sensitivity, specificity, positive (PPV and negative predictive values (NPV of the t-CWT were assessed and compared to a variety of competing procedures using simulated EEG data at six low signal-to-noise ratios. Results show that the t-CWT combines high sensitivity and specificity with favorable PPV and NPV. Applying the t-CWT to authentic EEG data obtained from 14 healthy participants confirmed its high sensitivity. The t-CWT may thus be well suited for the assessment of weak ERPs in single-subject settings.
Comparison of modal spectral and non-linear time history analysis of a piping system
International Nuclear Information System (INIS)
Gerard, R.; Aelbrecht, D.; Lafaille, J.P.
A typical piping system of the discharge line of the chemical and volumetric control system, outside the containment, between the penetration and the heat exchanger, an operating power plant was analyzed using four different methods: Modal spectral analysis with 2% constant damping, modal spectral analysis using ASME Code Case N411 (PVRC damping), linear time history analysis, non-linear time history analysis. This paper presents an estimation of the conservatism of the linear methods compared to the non-linear analysis. (orig./HP)
Numerical limitations in application of vector autoregressive modeling and Granger causality to analysis of EEG time series
Kammerdiner, Alla; Xanthopoulos, Petros; Pardalos, Panos M.
In this chapter a potential problem with application of the Granger-causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.
Analysis of behavioral and EEG correlatives of attention in the dynamics of recovery of consciousness following severe brain injury
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E. V. Sharova
Full Text Available Objective: to determine the behavioral manifestations and electroencephalographic correlates of modality-nonspecific attention using a clinical model of severe brain injury (SBI.Patients and methods. 35 patients with SBI in the dynamics of post-coma recovery of mental activity (a study group and 23 healthy subjects (a control group were examined. The behavioral manifestations of NSA from coma to clear consciousness were analyzed in the patients. Changes in the pattern of EEG and in the indices of its coherence in the presence and activation of different forms of attention (an orienting response to the sound and eye opening; involuntary and voluntary visual forms, by applying specially developed computerized techniques, were investigated. The features of associated with attention changes in interhemispheric EEG coherence (IHC with the data of 3T diffusion tensor tractography of the corpus callosum (CC were compared.Results. Attention disorders were shown to be essential and an «axial disorder» in patients with SBI. There were statistically confirmed qualitative and quantitative differences attention-associated changes in the EEG pattern and IHC in reversible and chronic unconsciousness. The important favorable prognostic sign proved to be reactive changes in interhemispheric EEG relations, including frontal ones characterized by the absence of clear external manifestations of consciousness in the very earliest stages. There was a significant correlation between the preservation of CC tracts (primarily, the rostrum, anterior portion, and splenium and attention-related reactivity of IHC, which reflects the specific, though nonrigid, structural determinacy of the latter.
Quantitative EEG and Current Source Density Analysis of Combined Antiepileptic Drugs and Dopaminergic Agents in Genetic Epilepsy: Two Case Studies.
Emory, Hamlin; Wells, Christopher; Mizrahi, Neptune
Two adolescent females with absence epilepsy were classified, one as attention deficit and the other as bipolar disorder. Physical and cognitive exams identified hypotension, bradycardia, and cognitive dysfunction. Their initial electroencephalograms (EEGs) were considered slightly slow, but within normal limits. Quantitative EEG (QEEG) data included relative theta excess and low alpha mean frequencies. A combined treatment of antiepileptic drugs with a catecholamine agonist/reuptake inhibitor was sequentially used. Both patients' physical and cognitive functions improved and they have remained seizure free. The clinical outcomes were correlated with statistically significant changes in QEEG measures toward normal Z-scores in both anterior and posterior regions. In addition, low resolution electromagnetic tomography (LORETA) Z-scored source correlation analyses of the initial and treated QEEG data showed normalized patterns, supporting a neuroanatomic resolution. This study presents preliminary evidence for a neurophysiologic approach to patients with absence epilepsy and comorbid disorders and may provide a method for further research. © EEG and Clinical Neuroscience Society (ECNS) 2014.
Analysis of EEG signals regularity in adults during video game play in 2D and 3D.
Khairuddin, Hamizah R; Malik, Aamir S; Mumtaz, Wajid; Kamel, Nidal; Xia, Likun
Video games have long been part of the entertainment industry. Nonetheless, it is not well known how video games can affect us with the advancement of 3D technology. The purpose of this study is to investigate the EEG signals regularity when playing video games in 2D and 3D modes. A total of 29 healthy subjects (24 male, 5 female) with mean age of 21.79 (1.63) years participated. Subjects were asked to play a car racing video game in three different modes (2D, 3D passive and 3D active). In 3D passive mode, subjects needed to wear a passive polarized glasses (cinema type) while for 3D active, an active shutter glasses was used. Scalp EEG data was recorded during game play using 19-channel EEG machine and linked ear was used as reference. After data were pre-processed, the signal irregularity for all conditions was computed. Two parameters were used to measure signal complexity for time series data: i) Hjorth-Complexity and ii) Composite Permutation Entropy Index (CPEI). Based on these two parameters, our results showed that the complexity level increased from eyes closed to eyes open condition; and further increased in the case of 3D as compared to 2D game play.
Stimulus-dependent deliberation process leading to a specific motor action demonstrated via a multi-channel EEG analysis
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Sonja eHenz
Full Text Available The aim of the study was to determine whether a deliberative process, leading to a motor action, is detectable in high density EEG recordings. Subjects were required to press one of two buttons. In a simple motor task the subject knew which button to press, whilst in a color-word Stroop task subjects had to press the right button with the right index finger when meaning and color coincided, or the left button with the left index finger when meaning and color were disparate.EEG recordings obtained during the simple motor task showed a sequence of positive (P and negative (N cortical potentials (P1-N1-P2 which are assumed to be related to the processing of the movement.The sequence of cortical potentials was similar in EEG recordings of subjects having to deliberate over how to respond, but the above sequence (P1-N1-P2 was preceded by slowly increasing negativity (N0, with N0 being assumed to represent the end of the deliberation process.Our data suggest the existence of neurophysiological correlates of deliberative processes.
Nonlinear analysis of a closed-loop tractor-semitrailer vehicle system with time delay
Liu, Zhaoheng; Hu, Kun; Chung, Kwok-wai
In this paper, a nonlinear analysis is performed on a closed-loop system of articulated heavy vehicles with driver steering control. The nonlinearity arises from the nonlinear cubic tire force model. An integration method is employed to derive an analytical periodic solution of the system in the neighbourhood of the critical speed. The results show that excellent accuracy can be achieved for the calculation of periodic solutions arising from Hopf bifurcation of the vehicle motion. A criterion is obtained for detecting the Bautin bifurcation which separates branches of supercritical and subcritical Hopf bifurcations. The integration method is compared to the incremental harmonic balance method in both supercritical and subcritical scenarios.
Analysis of the interplay of quantum phases and nonlinearity applied to dimers with anharmonic interactions
International Nuclear Information System (INIS)
Raghavan, S.
We extend our analysis of the effects of the interplay of quantum phases and nonlinearity to address saturation effects in small quantum systems. We find that initial phases dramatically control the dependence of self-trapping on initial asymmetry of quasiparticle population and can compete or act with nonlinearity as well as saturation effects. We find that there is a minimum finite saturation value in order to obtain self-trapping that crucially depends on the initial quasiparticle phases and present a detailed phase-diagram in terms of the control parameters of the system: nonlinearity and saturation. (author). 14 refs, 3 figs
Approximate Solutions of Nonlinear Partial Differential Equations by Modified q-Homotopy Analysis Method
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Shaheed N. Huseen
Full Text Available A modified q-homotopy analysis method (mq-HAM was proposed for solving nth-order nonlinear differential equations. This method improves the convergence of the series solution in the nHAM which was proposed in (see Hassan and El-Tawil 2011, 2012. The proposed method provides an approximate solution by rewriting the nth-order nonlinear differential equation in the form of n first-order differential equations. The solution of these n differential equations is obtained as a power series solution. This scheme is tested on two nonlinear exactly solvable differential equations. The results demonstrate the reliability and efficiency of the algorithm developed.
Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability.
Miller, Christopher B; Bartlett, Delwyn J; Mullins, Anna E; Dodds, Kirsty L; Gordon, Christopher J; Kyle, Simon D; Kim, Jong Won; D'Rozario, Angela L; Lee, Rico S C; Comas, Maria; Marshall, Nathaniel S; Yee, Brendon J; Espie, Colin A; Grunstein, Ronald R
To empirically derive and evaluate potential clusters of Insomnia Disorder through cluster analysis from polysomnography (PSG). We hypothesized that clusters would differ on neurocognitive performance, sleep-onset measures of quantitative ( q )-EEG and heart rate variability (HRV). Research volunteers with Insomnia Disorder (DSM-5) completed a neurocognitive assessment and overnight PSG measures of total sleep time (TST), wake time after sleep onset (WASO), and sleep onset latency (SOL) were used to determine clusters. From 96 volunteers with Insomnia Disorder, cluster analysis derived at least two clusters from objective sleep parameters: Insomnia with normal objective sleep duration (I-NSD: n = 53) and Insomnia with short sleep duration (I-SSD: n = 43). At sleep onset, differences in HRV between I-NSD and I-SSD clusters suggest attenuated parasympathetic activity in I-SSD (P insomnia clusters derived from cluster analysis differ in sleep onset HRV. Preliminary data suggest evidence for three clusters in insomnia with differences for sustained attention and sleep-onset q -EEG. Insomnia 100 sleep study: Australia New Zealand Clinical Trials Registry (ANZCTR) identification number 12612000049875. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347742. © 2016 Associated Professional Sleep Societies, LLC.
Assessment of the depth of anesthesia based on symbolic dynamics of the EEG
Tupaika, Nadine; Vallverdú Ferrer, Montserrat; Jospin, Mathieu; Jensen, Erik Weber; Struys, Michel M. R. F.; Vereecke, Hugo E. M.; Voss, Andreas; Caminal Magrans, Pere
Methodologies based on symbolic dynamics have successfully demonstrated to reflect the nonlinear behavior of biological signals. In the present study, symbolic dynamics was applied to the electroencephalogram (EEG) in order to describe the level of depth of anesthesia. The EEG was transformed to symbol sequences. Words of three symbols were built from this symbolic series. The results obtained from the EEGs of 36 patients undergoing anesthesia showed that the probabilities of the ...
Correlation between intra- and extracranial background EEG
DEFF Research Database (Denmark)
Duun-Henriksen, Jonas; Kjaer, Troels W.; Madsen, Rasmus E.
Scalp EEG is the most widely used modality to record the electrical signals of the brain. It is well known that the volume conduction of these brain waves through the brain, cerebrospinal fluid, skull and scalp reduces the spatial resolution and the signal amplitude. So far the volume conduction...... has primarily been investigated by realistic head models or interictal spike analysis. We have set up a novel and more realistic experiment that made it possible to compare the information in the intra- and extracranial EEG. We found that intracranial EEG channels contained correlated patterns when...... placed less than 30 mm apart, that intra- and extracranial channels were partly correlated when placed less than 40 mm apart, and that extracranial channels probably were correlated over larger distances. The underlying cortical area that influences the extracranial EEG is found to be up to 45 cm2...
Linear and nonlinear stability analysis, associated to experimental fast reactors. Part 2
International Nuclear Information System (INIS)
Amorim, E.S. do; Moura Neto, C. de; Rosa, M.A.P.
The nonlinear effects in fast reactors kinetics and their stability are studied. The Lyapunov criteria and the Lurie-Letov functions for nonlinear systems were established and simulated. Small oscillations were studied by a Fourier analysis to clarify particular aspects of feedback and load functions in fast reactor at zero power, or/and in normal power level. The results were in agreement with the experimental data existing in the literature. (E.G.) [pt
Non-linear analytic and coanalytic problems (Lp-theory, Clifford analysis, examples)
International Nuclear Information System (INIS)
Dubinskii, Yu A; Osipenko, A S
Two kinds of new mathematical model of variational type are put forward: non-linear analytic and coanalytic problems. The formulation of these non-linear boundary-value problems is based on a decomposition of the complete scale of Sobolev spaces into the 'orthogonal' sum of analytic and coanalytic subspaces. A similar decomposition is considered in the framework of Clifford analysis. Explicit examples are presented
Non-linear analytic and coanalytic problems ( L_p-theory, Clifford analysis, examples)
Dubinskii, Yu A.; Osipenko, A. S.
Two kinds of new mathematical model of variational type are put forward: non-linear analytic and coanalytic problems. The formulation of these non-linear boundary-value problems is based on a decomposition of the complete scale of Sobolev spaces into the "orthogonal" sum of analytic and coanalytic subspaces. A similar decomposition is considered in the framework of Clifford analysis. Explicit examples are presented.
Stability analysis of solutions to nonlinear stiff Volterra functional differential equations in Banach spaces
Institute of Scientific and Technical Information of China (English)
LI Shoufu
A series of stability, contractivity and asymptotic stability results of the solutions to nonlinear stiff Volterra functional differential equations (VFDEs) in Banach spaces is obtained, which provides the unified theoretical foundation for the stability analysis of solutions to nonlinear stiff problems in ordinary differential equations(ODEs), delay differential equations(DDEs), integro-differential equations(IDEs) and VFDEs of other type which appear in practice.
Analysis of boundary layer flow over a porous nonlinearly stretching sheet with partial slip at
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Swati Mukhopadhyay
Full Text Available The boundary layer flow of a viscous incompressible fluid toward a porous nonlinearly stretching sheet is considered in this analysis. Velocity slip is considered instead of no-slip condition at the boundary. Similarity transformations are used to convert the partial differential equation corresponding to the momentum equation into nonlinear ordinary differential equation. Numerical solution of this equation is obtained by shooting method. It is found that the horizontal velocity decreases with increasing slip parameter.
Stability analysis of nonlinear autonomous systems - General theory and application to flutter
Smith, L. L.; Morino, L.
The analysis makes use of a singular perturbation method, the multiple time scaling. Concepts of stable and unstable limit cycles are introduced. The solution is obtained in the form of an asymptotic expansion. Numerical results are presented for the nonlinear flutter of panels and airfoils in supersonic flow. The approach used is an extension of a method for analyzing nonlinear panel flutter reported by Morino (1969).
Nonlinear Finite Element Analysis of a General Composite Shell
for (t) in Equation (B.15) (Appendix B) and writes it as a function of displacements for I the nonlinear problem one obtains [8] 3 29 (*(a)) - [K(a...linked to the main program before execution. Isubroutine upress(t,pa,pb,iunit, ielt ,x,y,z,live,press) c c Pressure distribution subroutine for c...then compiled and linked to the main program before execution. I SUBROUTINE UPRESS(T,PA,PB,IUNIT, IELT ,X,Y,Z,LIVE,PRESS) C c Pressure distribution
Linear and nonlinear analysis of density wave instability phenomena
International Nuclear Information System (INIS)
Ambrosini, Walter
In this paper the mechanism of density-wave oscillations in a boiling channel with uniform and constant heat flux is analysed by linear and nonlinear analytical tools. A model developed on the basis of a semi-implicit numerical discretization of governing partial differential equations is used to provide information on the transient distribution of relevant variables along the channel during instabilities. Furthermore, a lumped parameter model and a distributed parameter model developed in previous activities are also adopted for independent confirmation of the observed trends. The obtained results are finally put in relation with the picture of the phenomenon proposed in classical descriptions. (author)
Bifurcation and stability analysis of a nonlinear milling process
Weremczuk, Andrzej; Rusinek, Rafal; Warminski, Jerzy
Numerical investigations of milling operations dynamics are presented in this paper. A two degree of freedom nonlinear model is used to study workpiece-tool vibrations. The analyzed model takes into account both flexibility of the tool and the workpiece. The dynamics of the milling process is described by the discontinuous ordinary differential equation with time delay, which can cause process instability. First, stability lobes diagrams are created on the basis of the parameters determined in impact test of an end mill and workpiece. Next, the bifurcations diagrams are performed for different values of rotational speeds.