A practical guide to the selection of independent components of the electroencephalogram for artifact correction

Journal of Neuroscience Methods
Maximilien ChaumonNiko A Busch

Abstract

Electroencephalographic data are easily contaminated by signals of non-neural origin. Independent component analysis (ICA) can help correct EEG data for such artifacts. Artifact independent components (ICs) can be identified by experts via visual inspection. But artifact features are sometimes ambiguous or difficult to notice, and even experts may disagree about how to categorise a particular component. It is therefore important to inform users on artifact properties, and give them the opportunity to intervene. Here we first describe artifacts captured by ICA. We review current methods to automatically select artifactual components for rejection, and introduce the SASICA software, implementing several novel selection algorithms as well as two previously described automated methods (ADJUST, Mognon et al. Psychophysiology 2011;48(2):229; and FASTER, Nolan et al. J Neurosci Methods 2010;48(1):152). We evaluate these algorithms by comparing selections suggested by SASICA and other methods to manual rejections by experts. The results show that these methods can inform observers to improve rejections. However, no automated method can accurately isolate artifacts without supervision. The comprehensive and interactive plots produced by...Continue Reading

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Citations

Oct 21, 2015·Journal of Neuroscience Methods·Rodolfo AbreuPatrícia Figueiredo
Mar 26, 2016·Frontiers in Human Neuroscience·Julian KeilDaniel Senkowski
Jul 11, 2016·International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology·An T NguyenAllison M Fox
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Jan 5, 2020·Cognitive, Affective & Behavioral Neuroscience·Martin WeißJohannes Hewig
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May 4, 2016·Journal of Neural Engineering·Anderson S OliveiraDaniel P Ferris
Apr 14, 2020·PloS One·Daniel S KlugerRicarda I Schubotz

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