Toward the Understanding of Topographical and Spectral Signatures of Infant Movement Artifacts in Naturalistic EEG.

electroencephalography infants motion artifacts naturalistic paradigm signal distortion

Journal

Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481

Informations de publication

Date de publication:
2020
Historique:
received: 05 02 2019
accepted: 23 03 2020
entrez: 16 5 2020
pubmed: 16 5 2020
medline: 16 5 2020
Statut: epublish

Résumé

Electroencephalography (EEG) is perhaps the most widely used brain-imaging technique for pediatric populations. However, EEG signals are prone to distortion by motion. Compared to adults, infants' motion is both more frequent and less stereotypical yet motion effects on the infant EEG signal are largely undocumented. Here, we present a systematic assessment of naturalistic motion effects on the infant EEG signal. EEG recordings were performed with 14 infants (12 analyzed) who passively watched movies whilst spontaneously producing periods of bodily movement and rest. Each infant produced an average of 38.3 s (SD = 14.7 s) of rest and 18.8 s (SD = 17.9 s) of single motion segments for the final analysis. Five types of infant motions were analyzed: Jaw movements, and Limb movements of the Hand, Arm, Foot, and Leg. Significant movement-related distortions of the EEG signal were detected using cluster-based permutation analysis. This analysis revealed that, relative to resting state, infants' Jaw and Arm movements produced significant

Identifiants

pubmed: 32410940
doi: 10.3389/fnins.2020.00352
pmc: PMC7199478
doi:

Types de publication

Journal Article

Langues

eng

Pagination

352

Informations de copyright

Copyright © 2020 Georgieva, Lester, Noreika, Yilmaz, Wass and Leong.

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Auteurs

Stanimira Georgieva (S)

Department of Psychology, University of Cambridge, Cambridge, United Kingdom.

Suzannah Lester (S)

Department of Psychology, University of Cambridge, Cambridge, United Kingdom.

Valdas Noreika (V)

Department of Psychology, University of Cambridge, Cambridge, United Kingdom.

Meryem Nazli Yilmaz (MN)

Department of Psychology, University of Cambridge, Cambridge, United Kingdom.

Sam Wass (S)

Department of Psychology, University of East London, London, United Kingdom.

Victoria Leong (V)

Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
Division of Psychology, Nanyang Technological University, Singapore, Singapore.

Classifications MeSH