Integrating spatial and temporal features for enhanced artifact removal in multi-channel EEG recordings
Jin Yin,
Aiping Liu,
Lanlan Wang
et al.
Abstract:Objective. Various artifacts in electroencephalography (EEG) are a big hurdle to prevent brain–computer interfaces from real-life usage. Recently, deep learning-based EEG denoising methods have shown excellent performance. However, existing deep network designs inadequately leverage inter-channel relationships in processing multi-channel EEG signals. Typically, most methods process multi-channel signals in a channel-by-channel way. Considering the correlations among EEG channels during the same brain activity,… Show more
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