A Review on Detection and Correction of Artifacts from EEG Data

Abstract

Electroencephalography (EEG) offers a wide range of uses in a variety of industries. Low SNR (signal to noise ratios), nevertheless, limit EEG applicability. EEG noise is caused by a variety of artifacts and numerous strategies have already been developed to identify and eliminate these inconsistencies. Various methods differ from merely identifying and discarding artifact ridden segments to isolating the EEG signal's noise content. With an emphasis on the previous half decade, we discuss a range of contemporary and traditional strategies for EEG data artifact recognition and removal. We assess the approaches' merits and drawbacks before proposing potential prospects for the area.