EEG artifact suppression using a Wasserstein generative adversarial autoencoder

Abstract EEG recordings are highly susceptible to artifacts such as ocular (EOG), muscular (EMG), and environmental noise, which degrade signal quality and hinder downstream analysis. In this work, we propose a Wasserstein Generative Adversarial Autoencoder (WGAN-AE) that jointly optimizes signal reconstruction and distribution alignment to achieve artifact suppression while preserving physiologic…