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.

翻译：静息态脑电图提供了一种非侵入性观察自发脑活动的方式，但高质量数据的有限可用性以及对人工设计脑电图特征的严重依赖，常常制约了有意义模式的提取。生成对抗网络不仅为合成和增强神经信号提供了手段，也为直接从原始数据中学习有意义的表征提供了一种有前景的途径，这种双重能力在脑电图研究中仍很大程度上未被探索。在本研究中，我们引入了一个基于生成对抗网络的可扩展框架，用于静息态脑电图，该框架承担双重角色：1）合成和2）无监督特征提取。生成的时序数据紧密复现了真实脑电图的关键统计和频谱特性，这一点已通过视觉和定量评估得到验证。重要的是，我们证明该模型学习到的表征可重新用于下游性别分类任务，其样本外准确率高于直接在脑电图信号上训练的模型，并与近期脑电图基础模型表现相当，同时使用了显著更少的数据和计算资源。这些发现突显了生成模型作为神经信号生成器和无监督特征提取器的潜力，为更数据高效、架构驱动的脑电图分析方法铺平了道路，减少了对人工特征工程的依赖。本研究的实现代码可在以下网址获取：https://github.com/Yeganehfrh/YARE-GAN。