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dc.contributor.authorMarriott Haresign, I
dc.contributor.authorPhillips, E
dc.contributor.authorWhitehorn, M
dc.contributor.authorNoreika, V
dc.contributor.authorJones, EJH
dc.contributor.authorLeong, V
dc.contributor.authorWass, SV
dc.date.accessioned2022-01-07T00:31:47Z
dc.date.available2022-01-07T00:31:47Z
dc.date.issued2021-12
dc.identifier.issn1878-9293
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332262
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.
dc.format.mediumPrint-Electronic
dc.publisherElsevier BV
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectArtifact correction
dc.subjectDeep learning
dc.subjectEEG
dc.subjectEvent-related potentials (ERP)
dc.subjectIndependent component analysis (ICA)
dc.titleAutomatic classification of ICA components from infant EEG using MARA.
dc.typeArticle
dc.publisher.departmentDepartment of Psychology
dc.date.updated2022-01-06T11:39:24Z
prism.numberARTN 101024
prism.publicationDate2021
prism.publicationNameDev Cogn Neurosci
prism.startingPage101024
prism.volume52
dc.identifier.doi10.17863/CAM.79707
dcterms.dateAccepted2021-10-18
rioxxterms.versionofrecord10.1016/j.dcn.2021.101024
rioxxterms.versionVoR
dc.identifier.eissn1878-9307
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEconomic and Social Research Council (ES/N006461/1)
cam.issuedOnline2021-10-20
cam.depositDate2022-01-06
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial-NoDerivatives 4.0 International