Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events

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. B eing able to generate EEG data computationally could address this limitation . We propose a novel Wasserstein Generative Adversarial Network with gradient penalty ( W GAN - GP) to synthesize EEG data. We further extend ed 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 demonstrate d the validity of the generated samples . We also tested intra - subject cross - session classification performance for classifying the RSVP target events and show ed that class - conditioned W GAN - GP can achieve improved event - classification performance over EEGNet . LECTROENCEPHAL OGRAPHY (EEG) i s an attractive neuroimaging tool for measuring brain activities due to its portability, noninvasiveness and its ability to capture spatiotemporal dynamics of human brains . However, obtaining high - quality EEG data could be labor - intensive an d costly. The scarcity of high - quality EEG data poses significant challenges in the era of deep learning (DL) to train high - performing deep models to predict cognitive events and understand associated brain dynamics and mechanisms. It is thus of great interest in developing cost - effective approaches to augment the limited EEG samples so that the superb ability of DL in learning data representation can be fully exploited for EEG - based cognitive event classification.