  Electroencephalography (EEG) data are difficult to obtain due to complex
experimental setups and reduced comfort with prolonged wearing. This poses
challenges to train powerful deep learning model with the limited EEG data.
Being able to generate EEG data computationally could address this limitation.
We propose a novel Wasserstein Generative Adversarial Network with gradient
penalty (WGAN-GP) to synthesize EEG data. This network addresses several
modeling challenges of simulating time-series EEG data including frequency
artifacts and training instability. We further extended this network to a
class-conditioned variant that also includes a classification branch to perform
event-related classification. We trained the proposed networks to generate one
and 64-channel data resembling EEG signals routinely seen in a rapid serial
visual presentation (RSVP) experiment and demonstrated the validity of the
generated samples. We also tested intra-subject cross-session classification
performance for classifying the RSVP target events and showed that
class-conditioned WGAN-GP can achieve improved event-classification performance
over EEGNet.
