YARE-GAN: Yet Another Resting State EEG-GAN

Resting-state EEG offers a non-invasive view of spontaneous brain activity, yet the extraction of meaningful patterns is often constrained by limited availability of high-quality data, and heavy reliance on manually engineered EEG features. Generative Adversarial Networks (GANs) offer not only a means to synthesize and augment neural signals, but also a promising way for learning meaningful representations directly from raw data, a dual capability that remains largely unexplored in EEG research. In this study, we introduce a scalable GAN-based framework for resting-state EEG that serves this dual role: 1) synthesis and 2) unsupervised feature extraction. The generated time series closely replicate key statistical and spectral properties of real EEG, as validated through both visual and quantitative evaluations. Importantly, we demonstrate that the model's learned representations can be repurposed for a downstream gender classification task, achieving higher out-of-sample accuracy than models trained directly on EEG signals and performing comparably to recent EEG foundation models, while using significantly less data and computational resources. These findings highlight the potential of generative models to serve as both neural signal generators and unsupervised feature extractors, paving the way for more data-efficient, architecture-driven approaches to EEG analysis with reduced reliance on manual feature engineering. The implementation code for this study is available at: https://github.com/Yeganehfrh/YARE-GAN.