Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153617
Title: Automatic classification of ICA components from infant EEG using MARA
Authors: Marriott Haresign, I.
Phillips, E.
Whitehorn, M.
Noreika, V.
Jones, Emma-Jane
Leong, Victoria
Wass, S. V.
Keywords: Social sciences::General
Issue Date: 2021
Source: Marriott 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.2021.101024
Journal: Developmental Cognitive Neuroscience
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.
URI: https://hdl.handle.net/10356/153617
ISSN: 1878-9293
DOI: 10.1016/j.dcn.2021.101024
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/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

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