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Limiting data loss in infant EEG: putting hunches to the test
EEG is a widely used tool to study the infant brain and its relationship with behavior. As infants usually have small attention spans, move at free will, and do not respond to task instructions, attrition rates are usually high. Increasing our understanding of what influences data loss is therefore...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7358181/ https://www.ncbi.nlm.nih.gov/pubmed/32658760 http://dx.doi.org/10.1016/j.dcn.2020.100809 |
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author | van der Velde, Bauke Junge, Caroline |
author_facet | van der Velde, Bauke Junge, Caroline |
author_sort | van der Velde, Bauke |
collection | PubMed |
description | EEG is a widely used tool to study the infant brain and its relationship with behavior. As infants usually have small attention spans, move at free will, and do not respond to task instructions, attrition rates are usually high. Increasing our understanding of what influences data loss is therefore vital. The current paper examines external factors to data loss in a large-scale on-going longitudinal study (the YOUth project; 1279 five-month-olds, 1024 ten-months-olds, and 109 three-year-olds). Data loss is measured for both continuous EEG and ERP tasks as the percentage data loss after artifact removal. Our results point to a wide array of external factors that contribute to data loss, some related to the child (e.g., gender; age; head shape) and some related to experimental settings (e.g., choice of research assistant; time of day; season; and course of the experiment). Data loss was also more pronounced in the ERP experiment than in the EEG experiment. Finally, evidence was found for within-subject stability in data loss characteristics over multiple sessions. We end with recommendations to limit data loss in future studies. |
format | Online Article Text |
id | pubmed-7358181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-73581812020-07-17 Limiting data loss in infant EEG: putting hunches to the test van der Velde, Bauke Junge, Caroline Dev Cogn Neurosci Articles from the Special Issue on Teaming up to understand individual development; Edited by Chantal Kemner, Angela Sarabdjitsingh, Margot Peeters, Eveline de Zeeuw, Stefanie Nelemans, Anna van Duijvenvoord. EEG is a widely used tool to study the infant brain and its relationship with behavior. As infants usually have small attention spans, move at free will, and do not respond to task instructions, attrition rates are usually high. Increasing our understanding of what influences data loss is therefore vital. The current paper examines external factors to data loss in a large-scale on-going longitudinal study (the YOUth project; 1279 five-month-olds, 1024 ten-months-olds, and 109 three-year-olds). Data loss is measured for both continuous EEG and ERP tasks as the percentage data loss after artifact removal. Our results point to a wide array of external factors that contribute to data loss, some related to the child (e.g., gender; age; head shape) and some related to experimental settings (e.g., choice of research assistant; time of day; season; and course of the experiment). Data loss was also more pronounced in the ERP experiment than in the EEG experiment. Finally, evidence was found for within-subject stability in data loss characteristics over multiple sessions. We end with recommendations to limit data loss in future studies. Elsevier 2020-06-26 /pmc/articles/PMC7358181/ /pubmed/32658760 http://dx.doi.org/10.1016/j.dcn.2020.100809 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Articles from the Special Issue on Teaming up to understand individual development; Edited by Chantal Kemner, Angela Sarabdjitsingh, Margot Peeters, Eveline de Zeeuw, Stefanie Nelemans, Anna van Duijvenvoord. van der Velde, Bauke Junge, Caroline Limiting data loss in infant EEG: putting hunches to the test |
title | Limiting data loss in infant EEG: putting hunches to the test |
title_full | Limiting data loss in infant EEG: putting hunches to the test |
title_fullStr | Limiting data loss in infant EEG: putting hunches to the test |
title_full_unstemmed | Limiting data loss in infant EEG: putting hunches to the test |
title_short | Limiting data loss in infant EEG: putting hunches to the test |
title_sort | limiting data loss in infant eeg: putting hunches to the test |
topic | Articles from the Special Issue on Teaming up to understand individual development; Edited by Chantal Kemner, Angela Sarabdjitsingh, Margot Peeters, Eveline de Zeeuw, Stefanie Nelemans, Anna van Duijvenvoord. |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7358181/ https://www.ncbi.nlm.nih.gov/pubmed/32658760 http://dx.doi.org/10.1016/j.dcn.2020.100809 |
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