Key result
Review assesses contemporary and traditional strategies for EEG artifact removal to propose future prospects.
Why the study?
Low signal-to-noise ratios caused by diverse artifacts limit the applicability of electroencephalography.
This review provides an overview of methods for detecting and correcting artifacts in EEG data to improve signal-to-noise ratio.
May guide EEG artifact mitigation in neurophysiology; leaves open empirical validation of proposed strategies.
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.
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Motdhare et al. (2023) conducted a review in EEG artifacts. Artifact recognition and removal strategies was evaluated. This review discusses contemporary and traditional strategies for EEG data artifact recognition and removal, assessing their merits and drawbacks to propose potential prospects for the field.
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