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Multi-Algorithm Artifact Correction (MAAC) procedure part one: Algorithm and example.

Authors :
Dien J
Source :
Biological psychology [Biol Psychol] 2024 Apr; Vol. 188, pp. 108775. Date of Electronic Publication: 2024 Mar 16.
Publication Year :
2024

Abstract

The Multi-Algorithm Artifact Correction (MAAC) procedure is presented for electroencephalographic (EEG) data, as made freely available in the open-source EP Toolkit (Dien, 2010). First the major EEG artifact correction methods (regression, spatial filters, principal components analysis, and independent components analysis) are reviewed. Contrary to the dominant approach of picking one method that is thought to be most effective, this review concludes that none are globally superior, but rather each has strengths and weaknesses. Then each of the major artifact types are reviewed (Blink, Corneo-Retinal Dipole, Saccadic Spike Potential, and Movement). For each one, it is proposed that one of the major correction methods is best matched to address it, resulting in the MAAC procedure. The MAAC itself is then presented, as implemented in the EP Toolkit, in order to provide a sense of the user experience. The primary goal of this present paper is to make the conceptual argument for the MAAC approach.<br />Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2024 Elsevier B.V. All rights reserved.)

Details

Language :
English
ISSN :
1873-6246
Volume :
188
Database :
MEDLINE
Journal :
Biological psychology
Publication Type :
Academic Journal
Accession number :
38499226
Full Text :
https://doi.org/10.1016/j.biopsycho.2024.108775