Baseline-Guided Representation Learning for Noise-Robust EEG Signal Classification.

Brain-computer interfaces (BCIs) suffer from limited accuracy due to noisy electroencephalography (EEG) signals. Existing denoising methods often remove artifacts such as eye movement or use techniques such as linear detrending, which inadvertently discard crucial task-relevant information. To address this issue, we present BGNet, a novel deep learning framework that leverages underutilized baseline EEG signals for dynamic noise mitigation and robust feature extraction to improve motor imagery (MI) EEG classification.