there is at present no certain diagnostic test for alzheimer 's disease ( ad ) . much attention is devoted to identify specific markers for ad and its presumed precursor stage , amnestic mild cognitive impairment ( mci ) . one research technique concerns the electroencephalogram ( eeg ) , which has the advantages of easy availability and low cost . research eeg efforts focused on the search for parameters that may help distinguish between healthy ageing , mci , and ad . ad is characterised by slowing of the eeg , which finding can be quantified using spectral analysis . resulting variables such as ratios between fast and slow activity , alpha and theta relative power , or the mean frequency attempts have been made to increase the differences between groups by challenging the subjects , such as by a memory task or more simply by comparing for example , studies comparing ad with healthy controls reported increased theta power and decreased alpha suppression during eye opening and a memory task [ 1 , 2 , 46 ] . compared to controls , eeg abnormalities in mci consisted of decreased alpha suppression and increased alpha power during a memory task [ 7 , 8 ] . such reports suggest that the eeg may have an ancillary role in the diagnostic workup of ad and mci , but it has not achieved a prominent place in the diagnostic arsenal . many attempts have been made to improve the yield of the eeg for this purpose . it is commonly considered of the utmost importance to obtain a high - quality eeg , uncontaminated by artifacts such as blinks , eye movements , and electromyographic ( emg ) activity , which can be extremely difficult in dementia . eye blinks and movements most often cause low frequency potentials , and may therefore affect the measurement of low frequency signals in the eeg , that is , delta and theta waves [ 10 , 11 ] . emg activity is usually due to activity of scalp , facial , and jaw muscles and typically most strongly affects frontal and temporal eeg recordings . it mostly affects the high - frequency gamma and beta bands . over the years researchers devoted much attention to dealing with these unwanted influences . one approach is to reject contaminated epochs from analysis , either manually or through automated methods [ 2 , 10 , 13 ] . others attempted to negate effects of artifacts using various techniques [ 1418 ] , and yet others chose to simply disregard affected frequency bands , largely leaving theta and alpha activity . the rationale behind these intensive efforts to remove eye blinks , movements , and emg from the eeg is probably that the latter reflects  pure  cerebral activity , whereas the  artifacts  do not . we do not doubt that the eeg reflects cerebral activity , but wondered whether the other group of parameters can be dismissed out of hand . they may be regarded as behavioural correlates , and ad ultimately affects behaviour , in a chain of events involving amongst others biochemistry , neuronal function , and cognition . it is conceivable that the amount of muscle activity is related to the degree of cognitive impairment or to neuropsychiatric symptoms such as agitation , irritability , and depression . do so has , to our knowledge , not been tested .   in this pilot study , we explored the discriminative value of quantitative eeg and emg in distinguishing between ad , mci and healthy aging . thirteen age - matched controls , 13 patients diagnosed with mci , and 11 diagnosed with probable ad   participated in the study . patients had been referred to the outpatient memory clinic of the leiden university medical centre . control subjects without cognitive complaints were recruited through an advertisement in a local newspaper . all patients and controls underwent general medical , neurological , neuropsychological , and brain mri investigations as part of the standardized dementia screening . furthermore , all patient histories were reviewed and diagnoses reached in multidisciplinary consensus meetings . within three months from the standardized dementia screening , patients and controls participated in an additional eeg examination . eligible subjects had to be free of psychotropic medication , aged 60 years or above , and without previous history of psychiatric and neurological disorders or substance abuse . moreover , they had no abnormalities on mri other than white matter hyper intensities or an incidental small lacunar lesion ( 5  mm diameter ) . written informed consent was obtained from all subjects , or from close relatives or caregivers in case of dementia . the geriatric depression scale ( gds ) , a patient - based interview , was used to determine the presence and severity of depressive feelings . we used the neuropsychiatric inventory ( npi - q ) , an informant - based interview , to determine the presence and severity of neuropsychiatric problems ( i.e. , delusions , hallucinations , agitation , depression , anxiety , euphoria , apathy , disinhibition , irritability , aberrant motor activity , night - time behavioural disturbances , and eating disturbances ) . per item a score from  0  to  3  could be obtained , with  0  indicating the absence of a neuropsychiatric problem and  1  ,  2  , and  3  indicating a mild , moderate , and severe neuropsychiatric problem , respectively . three factors were used ( 1 ) a  mood / apathy  factor including depression , apathy , night - time behavioural disturbances , and eating disturbances , ( 2 ) a  hyperactivity factor  including agitation , euphoria , irritability , disinhibition , and aberrant motor activity , and ( 3 ) a  psychosis  factor including hallucinations and delusions . anxiety was considered a separate factor , but it was excluded from further analysis because of limited variability in scores . the cambridge cognitive examination ( camcog ) eegs were recorded using a nihon kohden 2110 apparatus with 21 ag / agcl electrodes placed according to the 10/20 system . ecg , respiration , and horizontal eye movement leads were recorded to facilitate recognition of artifacts . the eeg was band - pass filtered from 0.1670  hz for display and analysis but recorded unfiltered . the average reference montage was used , with the exclusion of electrodes fp1 , fp2 , a1 , and a2 . all eegs were recorded in the afternoon . during recording , subjects sat slightly reclined in a comfortable chair , approximately 1.5  m in front of a computer screen . the eeg was registered during a 10-minute eyes - closed period , a 3-minute eyes - open period , and during memory activation . the memory activation task concerned picture memory . each picture was presented for two seconds and subjects were asked to name the shown objects aloud . after presentation of the pictures , subjects had to close their eyes and memorise the pictures for 15 seconds . subjects were then asked to open their eyes and name as many pictures they could remember . the task was performed three times using the same 10 pictures . the number of remembered pictures was noted . to study eeg parameters , we selected specific periods of the eeg using visual analysis . sample selection was performed by the first author and corroborated by an experienced clinical neurophysiologist . both were blind to clinical diagnosis . samples had to be 48 seconds in length and were selected during conventional eyes closed and eyes open , and memory activation . frequency analysis was performed using a fast fourier transformation . we calculated absolute power in theta ( 48  hz ) and alpha ( 813  hz ) frequency bands . three parameters were then calculated : theta relative power during eyes closed , and alpha reactivity during both eye opening and memory activation . alpha reactivity is defined as the percentile decrease in absolute alpha power as compared to the eyes closed condition . eeg parameters were averaged over all electrodes and over the three selected eeg samples available for each of the eyes closed , eyes open , and memory activation periods . we chose these eeg parameters as they were found sensitive to ad and mci in previous research [ 1 , 2 , 46 , 8 ] . all signal processing was performed using matlab ( the mathworks , natick , usa ) . to measure the amount of emg activity , we did not select specific periods but used the entire period corresponding to the eyes closed , eyes open , and each of the three memory activation periods . the only selection involved was that periods in which the tasks were compromised by matters such as electrode correction were omitted . frequency analysis was performed using a fast fourier transformation . as a measure of emg activity , absolute power was calculated in the high frequency band ( 5070  hz ) and averaged over temporal ( t3 , t4 , t5 , and t6 ) and frontal electrodes ( f3 , f4 , f7 , and f8 ) . these sites are sensitive to emg activity from scalp , facial , and jaw muscles . the 50  hz threshold was chosen to exclude any possible confusion with brain activity , that is , beta or gamma activity , that might be present at lower frequencies . in doing so , we aimed to ensure that the measured activity was the result of muscle activity only .    for analysis , we used temporal and frontal emg during eyes closed , eyes open , and memory activation . group differences in cognitive and neuropsychiatric outcomes , eeg , and emg activity were assessed using anova with post hoc bonferroni tests . when data were not normally distributed we used two - sample kolmogorov - smirnov tests to assess group differences . subsequently , we performed a two - tailed parametric or nonparametric correlation analysis to investigate relations between eeg and emg activity on the one hand and cognitive and neuropsychiatric parameters on the other . we did not investigate correlations in specific diagnostic groups because of the limited number of subjects per group . the aim of the study was to compare not only whether eeg and emg parameters differed between groups but also whether they did so to different degrees . we assumed they could not be compared directly , because of wholly different distribution patterns including various nonnormal distributions . the area under the curve serves as a quantitative indicator of how well a specific parameter distinguishes between groups , irrespective of the nature of the parameter in question . this value usually ranges between 0.5 ( meaning the parameter does not distinguish between the groups at all ) to 1.0 ( the parameter separates the two groups perfectly ) . sex , age , years of education , and gds scores did not differ between groups . group differences were found in camcog scores ( f(2,34 ) = 50.0 ; p < .001 ) . post hoc tests showed that ad patients had significantly lower camcog scores than controls and mci patients ( p < .001 ) . furthermore , mci patients had lower camcog scores ( p < .001 ) than controls . there were also differences in the number of pictures correctly remembered in the memory task used during eeg registration ( f(2,34 ) = 72.6 ; p < .001 ) . post hoc tests showed that ad and mci patients remembered less pictures than controls ( p < .001 ) . furthermore , ad patients had a lower score than mci patients ( p < .01 ) . group differences in theta relative power were found during eyes closed ( f(2,34 ) = 10.6 ; p < .001 ) . post hoc tests indicated that ad patients showed significantly higher theta relative power as compared with controls and mci patients ( both p < .001 ) . alpha reactivity upon eyes opening and memory activation also differed between groups ( f(2,34 ) = 5.3 ; p < .01 and ( f(2,34 ) = 6.1 ; p < .01 ) in that alpha reactivity was decreased in ad patients as compared with controls . group differences in frontal emg were found during both eyes closed and eyes open ( f(2,34 ) = 3.8 ; p < .05 and kolmogorov - smirnov z = 1.4 ; p < .05 ) . theta relative power , alpha reactivity during eyes open , and alpha reactivity during memory activation were related to camcog scores ; lower theta relative power and higher alpha reactivity indicated higher camcog scores , and thus better cognitive performance . furthermore , frontal emg during eyes open was related to camcog scores in that increased emg activity indicated lower camcog scores . correlations were found between , on the one hand , frontal emg during eyes closed , temporal emg during eyes closed , and temporal emg during eyes open and , on the other hand , gds scores ( see table 3 ) . in all cases , increased emg activity indicated more complaints of depression . no correlations were found between eeg and emg parameters and neuropsychiatric complaints on the npi . three of these concerned the comparison between ad patients and controls , and the fourth the ad - mci comparison . of the 18 emg rocs two concerned the ad - control comparison , and one the mci - control comparison . emg activity is usually regarded a source of problems in eegs used for patient care and research purposes in dementia , because the emg impairs the registration of the eeg . this is particularly relevant for activity at the high ( beta and gamma ) and low ( delta ) ends of the spectrum . the most important new finding of this pilot study is that emg activity could be not only a hindrance but also reflects some aspects of ad and mci . firstly , the amount of frontal emg activity was higher in the ad group than in controls , both during the eyes open and eyes closed conditions . as such , its presence is linked to the presence of ad , presumably through an association between facial muscle activity and the expression of emotions such as depression . we used this type of analysis to allow us to compare variables of a different nature , that is , eeg and emg . this comparison shows that eeg variables on the whole distinguish better between the groups than emg variables and also that emg variables are not worthless in this respect . in fact , for the distinction between mci and controls , it was an emg variable that best distinguished between groups . secondly , emg activity can not be considered as an aspecific finding : we found significant relations between emg activity and patient - based reports of depression . it , therefore , seems likely that the emg reflects disease - related neuropsychiatric changes . the relation between facial emg activity and depression has been studied previously : for instance , when depressed subjects generated sad thoughts or simply thought about a typical day in their current lives , the amount of facial emg activity increased . it can be imagined that a degree of depression in patients with memory complaints is expressed as an increased facial emg . as one might expect , different emotions lead to different patterns of emg activity in various muscles . in the context of an eeg , it seems likely that activity of the frontalis , corrugator supercilii , and zygomatic major muscles are picked up preferentially . note that we made no effort to record these muscles with any degree of precision , so we refrain from drawing detailed conclusions in this regard . it may well be argued that even if there is more emg activity in ad than in controls , this has little bearing on the understanding of the pathophysiology of ad . in part we agree ; the emg reflects muscle activation and is thus a behavioural correlate , while the eeg is a direct reflection of cerebral function . decreased eeg activity has been linked with functional disconnections among cortical areas , resulting among others from neuronal death and/or deficiency of neurotransmitters . this is no doubt true , but it should be kept in mind that it is doubtful which cerebral functions are expressed in the various rhythms of the eeg . for example , slowing of the eeg is not specific to the type of dementia , in spite of completely different pathophysiological substrates . in fact , eeg slowing is not even specific to dementia , as this also occurs in a large variety of other conditions ranging from mechanical trauma to intoxications . one may wonder whether eeg slowing is really so much closer to the pathology of ad than emg activity .      in the present study , the eeg generally resulted in a better discrimination between groups than the emg , except for the mci - control distinction , in which the emg performed better . we do not suggest that henceforth the emg should be used for discriminating mci patients and controls , as we made no attempt to optimise recordings for this purpose . for similar reasons , we did not calculate measures such as sensitivity and specificity even though many attempts have been made to optimise the eeg for this purpose . regarding optimisation , the best parameter might be the one that accurately reflects the presence of a pathogenic cause . an example is measuring the number of cag repeats in huntington 's disease , reflecting the pathogenetic cause . eeg parameters do not reflect the pathogenetic cause but the degree of affliction . as this varies from normal to this may be seen as a failure of the test to pick up any differences , but it seems more likely that the overlap truthfully represents an overlap in function . the best we can do may be to push against the detection boundary , and in this respect , it may pay to keep an open mind regarding the nature of the parameter to be measured .
many efforts have been directed at negating the influence of electromyographic ( emg ) activity on the eeg , especially in elderly demented patients . 
 we wondered whether these  artifacts  might reflect cognitive and behavioural aspects of dementia . in this pilot study 
 , 11 patients with probable alzheimer 's disease ( ad ) , 13 with amnestic mild cognitive impairment ( mci ) and 13 controls underwent eeg registration . 
 as emg measures , we used frontal and temporal 5070  hz activity . 
 we found that the eegs of ad patients displayed more theta activity , less alpha reactivity , and more frontal emg than controls . 
 interestingly , increased emg activity indicated more cognitive impairment and more depressive complaints . 
 eeg variables on the whole distinguished better between groups than emg variables , but an emg variable was best for the distinction between mci and controls . 
 our results suggest that emg activity in the eeg could be more than noise ; it differs systematically between groups and may reflect different cerebral functions than the eeg .