Automatic classification of ICA components from infant EEG using MARA. (December 2021)

• Record Type: Journal Article
• Authors: Marriott Haresign, I.; Phillips, E.; Whitehorn, M.; Noreika, V.; Jones, E.J.H.; Leong, V.; Wass, S.V.
• Is Part Of: Developmental cognitive neuroscience. Volume 52(2021)
• Publication Date: 2021-12
• Subjects: EEG -- Deep learning -- Artifact correction -- Independent component analysis (ICA) -- Event-related potentials (ERP); Cognitive neuroscience -- Periodicals; Developmental neurobiology -- Periodicals; Neuropsychology -- Periodicals; Neuropsychiatry -- Periodicals; 612.8233
• DOI: 10.1016/j.dcn.2021.101024
• Languages: English
• ISSNs: 1878-9293

Abstract

Abstract: Automated 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. Highlights: Currently, few tools are available for ICA based data correction that are designed around infant EEG. ICA correction is necessary for naturalistic paradigms when movement artifacts covary with cognitive processes. We present a system for automated ICA classification designed around infant EEG data. This system offers increased performance compared to systems of automatic ICA classification designed around adult EEG.