YARE-GAN: Yet Another Resting State EEG-GAN
This is an incremental study applying an existing GAN method to EEG data, potentially reducing manual feature engineering for neuroscience applications.
The authors implemented a Wasserstein GAN with Gradient Penalty to generate multi-channel resting-state EEG data, finding it effectively captured statistical and spectral characteristics but struggled with high-frequency frontal oscillations. They also showed the Critic's learned representations could be reused for gender classification, achieving significantly better out-of-sample accuracy than baselines.
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.