EOG Artifact Removal from EEG Using a Gated WGAN with Multi-Scale and Multi-Domain Feature Extraction

Background: Electroencephalogram (EEG) signals provide important information for brain–computer interfaces (BCIs) and the diagnosis of neurological disorders but are susceptible to electrooculographic (EOG) artifacts. Traditional methods often rely on multichannel data or relatively long windows, limiting online applicability. Deep learning enables single-channel end-to-end artifact removal, but e…