Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153617
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dc.contributor.authorMarriott Haresign, I.en_US
dc.contributor.authorPhillips, E.en_US
dc.contributor.authorWhitehorn, M.en_US
dc.contributor.authorNoreika, V.en_US
dc.contributor.authorJones, Emma-Janeen_US
dc.contributor.authorLeong, Victoriaen_US
dc.contributor.authorWass, S. V.en_US
dc.date.accessioned2021-12-14T08:14:14Z-
dc.date.available2021-12-14T08:14:14Z-
dc.date.issued2021-
dc.identifier.citationMarriott Haresign, I., Phillips, E., Whitehorn, M., Noreika, V., Jones, E., Leong, V. & Wass, S. V. (2021). Automatic classification of ICA components from infant EEG using MARA. Developmental Cognitive Neuroscience, 52, 101024-. https://dx.doi.org/10.1016/j.dcn.2021.101024en_US
dc.identifier.issn1878-9293en_US
dc.identifier.urihttps://hdl.handle.net/10356/153617-
dc.description.abstractAutomated systems for identifying and removing non-neural ICA components are growing in popularity among EEG researchers of adult populations. Infant EEG data differs in many ways from adult EEG data, but there exists almost no specific system for automated classification of source components from paediatric populations. Here, we adapt one of the most popular systems for adult ICA component classification for use with infant EEG data. Our adapted classifier significantly outperformed the original adult classifier on samples of naturalistic free play EEG data recorded from 10 to 12-month-old infants, achieving agreement rates with the manual classification of over 75% across two validation studies (n = 44, n = 25). Additionally, we examined both classifiers' ability to remove stereotyped ocular artifact from a basic visual processing ERP dataset compared to manual ICA data cleaning. Here, the new classifier performed on level with expert manual cleaning and was again significantly better than the adult classifier at removing artifact whilst retaining a greater amount of genuine neural signal operationalised through comparing ERP activations in time and space. Our new system (iMARA) offers developmental EEG researchers a flexible tool for automatic identification and removal of artifactual ICA components.en_US
dc.language.isoenen_US
dc.relation.ispartofDevelopmental Cognitive Neuroscienceen_US
dc.rights© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.subjectSocial sciences::Generalen_US
dc.titleAutomatic classification of ICA components from infant EEG using MARAen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Social Sciencesen_US
dc.identifier.doi10.1016/j.dcn.2021.101024-
dc.description.versionPublished versionen_US
dc.identifier.pmid34715619-
dc.identifier.scopus2-s2.0-85117878992-
dc.identifier.volume52en_US
dc.identifier.spage101024en_US
dc.subject.keywordsArtifact Correctionen_US
dc.subject.keywordsDeep Learningen_US
dc.description.acknowledgementThis research was funded by a Project Grant from the Leverhulme Trust UK, number RPG-2018-281. We wish to thank Federica Lamagna and Martina Eliano for contributing to coding the data. We also wish to thank all families who participated in the research.en_US
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