YARE-GAN: Yet Another Resting State EEG-GAN

In this study, we implement a Wasserstein GAN with Gradient Penalty (WGAN-GP)
to generate multi-channel resting-state EEG data and assess the quality of the
synthesized signals through both visual and feature-based evaluations. Our
results indicate that the model effectively captures the statistical and
spectral characteristics of real EEG data, although challenges remain in
replicating high-frequency oscillations in the frontal region. Additionally, we
demonstrate that the Critic's learned representations can be reused for gender
classification task, achieving an out-of-sample accuracy, significantly better
than a shuffled-label baseline and a model trained directly on EEG data. These
findings suggest that generative models can serve not only as EEG data
generators but also as unsupervised feature extractors, reducing the need for
manual feature engineering. This study highlights the potential of GAN-based
unsupervised learning for EEG analysis, suggesting avenues for more
data-efficient deep learning applications in neuroscience.