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Automatic classification of ICA components from infant EEG using MARA

I. Marriot Haresign, E. Phillips, M. Whitehorn, View ORCID ProfileV. Noreika, E.J.H. Jones, V. Leong, S.V. Wass
doi: https://doi.org/10.1101/2021.01.22.427809
I. Marriot Haresign
1Department of Psychology, University of East London, London, UK
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  • For correspondence: u1434978@uel.ac.uk
E. Phillips
1Department of Psychology, University of East London, London, UK
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M. Whitehorn
1Department of Psychology, University of East London, London, UK
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V. Noreika
5Department of Biological and Experimental Psychology, School of Biological and Chemical Sciences, Queen Mary University of London, UK
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  • ORCID record for V. Noreika
E.J.H. Jones
4Centre for Brain and Cognitive Development, Birkbeck College, University of London, UK
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V. Leong
2Department of Experimental Psychology, University of Cambridge, Cambridge, UK
3School of Social Sciences, Nanyang Technological University, Singapore
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S.V. Wass
1Department of Psychology, University of East London, London, UK
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Abstract

Automated systems for identifying and removing non-neural ICA components are growing in popularity among adult EEG researchers. 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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/Ira-marriott/iMARA/tree/main

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 24, 2021.
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Automatic classification of ICA components from infant EEG using MARA
I. Marriot Haresign, E. Phillips, M. Whitehorn, V. Noreika, E.J.H. Jones, V. Leong, S.V. Wass
bioRxiv 2021.01.22.427809; doi: https://doi.org/10.1101/2021.01.22.427809
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Automatic classification of ICA components from infant EEG using MARA
I. Marriot Haresign, E. Phillips, M. Whitehorn, V. Noreika, E.J.H. Jones, V. Leong, S.V. Wass
bioRxiv 2021.01.22.427809; doi: https://doi.org/10.1101/2021.01.22.427809

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