Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on existing methodology for removing artifacts, e.g., ocular, cardiac, muscle artifacts. The datasets, simulation platforms, performance measures artifact removal methods in previous related research are summarized. advantages disadvantages each technique discussed, including regression method, filtering blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum (SSA), independent vector (IVA). Also, applications hybrid approaches presented, discrete - adaptive method (DWT-AFM), DWT-BSS, EMD-BSS, noise canceler (SSA-ANC), SSA-BSS, EMD-IVA. Finally, a comparative these is provided based their merits. result shows that can remove more effectively than individual methods.