YARE-GAN: Yet Another Resting State EEG-GAN

Generative Adversarial Networks (GANs) have shown promise in synthesising realistic neural data, yet their potential for unsupervised representation learning in resting-state EEG remains under explored. 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 synthesised 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 fine-tuned for age group classification, achieving an out-of-sample accuracy, significantly better than a shuffled-label baseline. 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.