Title: Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification,
Abstract: Despite significant recent progress in the area of Brain-Computer Interface,
there are numerous shortcomings associated with collecting
Electroencephalography (EEG) signals in real-world environments. These include,
but are not limited to, subject and session data variance, long and arduous
calibration processes and performance generalisation issues across
differentsubjects or sessions. This implies that many downstream applications,
including Steady State Visual Evoked Potential (SSVEP) based classification
systems, can suffer from a shortage of reliable data. Generating meaningful and
realistic synthetic data can therefore be of significant value in circumventing
this problem. We explore the use of modern neural-based generative models
trained on a limited quantity of EEG data collected from different subjects to
generate supplementary synthetic EEG signal vectors subsequently utilised to
train an SSVEP classifier. Extensive experimental analyses demonstrate the
efficacy of our generated data, leading to significant improvements across a
variety of evaluations, with the crucial task of cross-subject generalisation
improving by over 35% with the use of synthetic data.