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Detection of mind wandering using EEG: Within and across individuals

Mind wandering is often characterized by attention oriented away from an external task towards our internal, self-generated thoughts. This universal phenomenon has been linked to numerous disruptive functional outcomes, including performance errors and negative affect. Despite its prevalence and imp...

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Detalles Bibliográficos
Autores principales: Dong, Henry W., Mills, Caitlin, Knight, Robert T., Kam, Julia W. Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8115801/
https://www.ncbi.nlm.nih.gov/pubmed/33979407
http://dx.doi.org/10.1371/journal.pone.0251490
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author Dong, Henry W.
Mills, Caitlin
Knight, Robert T.
Kam, Julia W. Y.
author_facet Dong, Henry W.
Mills, Caitlin
Knight, Robert T.
Kam, Julia W. Y.
author_sort Dong, Henry W.
collection PubMed
description Mind wandering is often characterized by attention oriented away from an external task towards our internal, self-generated thoughts. This universal phenomenon has been linked to numerous disruptive functional outcomes, including performance errors and negative affect. Despite its prevalence and impact, studies to date have yet to identify robust behavioral signatures, making unobtrusive, yet reliable detection of mind wandering a difficult but important task for future applications. Here we examined whether electrophysiological measures can be used in machine learning models to accurately predict mind wandering states. We recorded scalp EEG from participants as they performed an auditory target detection task and self-reported whether they were on task or mind wandering. We successfully classified attention states both within (person-dependent) and across (person-independent) individuals using event-related potential (ERP) measures. Non-linear and linear machine learning models detected mind wandering above-chance within subjects: support vector machine (AUC = 0.715) and logistic regression (AUC = 0.635). Importantly, these models also generalized across subjects: support vector machine (AUC = 0.613) and logistic regression (AUC = 0.609), suggesting we can reliably predict a given individual’s attention state based on ERP patterns observed in the group. This study is the first to demonstrate that machine learning models can generalize to “never-seen-before” individuals using electrophysiological measures, highlighting their potential for real-time prediction of covert attention states.
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spelling pubmed-81158012021-05-24 Detection of mind wandering using EEG: Within and across individuals Dong, Henry W. Mills, Caitlin Knight, Robert T. Kam, Julia W. Y. PLoS One Research Article Mind wandering is often characterized by attention oriented away from an external task towards our internal, self-generated thoughts. This universal phenomenon has been linked to numerous disruptive functional outcomes, including performance errors and negative affect. Despite its prevalence and impact, studies to date have yet to identify robust behavioral signatures, making unobtrusive, yet reliable detection of mind wandering a difficult but important task for future applications. Here we examined whether electrophysiological measures can be used in machine learning models to accurately predict mind wandering states. We recorded scalp EEG from participants as they performed an auditory target detection task and self-reported whether they were on task or mind wandering. We successfully classified attention states both within (person-dependent) and across (person-independent) individuals using event-related potential (ERP) measures. Non-linear and linear machine learning models detected mind wandering above-chance within subjects: support vector machine (AUC = 0.715) and logistic regression (AUC = 0.635). Importantly, these models also generalized across subjects: support vector machine (AUC = 0.613) and logistic regression (AUC = 0.609), suggesting we can reliably predict a given individual’s attention state based on ERP patterns observed in the group. This study is the first to demonstrate that machine learning models can generalize to “never-seen-before” individuals using electrophysiological measures, highlighting their potential for real-time prediction of covert attention states. Public Library of Science 2021-05-12 /pmc/articles/PMC8115801/ /pubmed/33979407 http://dx.doi.org/10.1371/journal.pone.0251490 Text en © 2021 Dong et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Dong, Henry W.
Mills, Caitlin
Knight, Robert T.
Kam, Julia W. Y.
Detection of mind wandering using EEG: Within and across individuals
title Detection of mind wandering using EEG: Within and across individuals
title_full Detection of mind wandering using EEG: Within and across individuals
title_fullStr Detection of mind wandering using EEG: Within and across individuals
title_full_unstemmed Detection of mind wandering using EEG: Within and across individuals
title_short Detection of mind wandering using EEG: Within and across individuals
title_sort detection of mind wandering using eeg: within and across individuals
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8115801/
https://www.ncbi.nlm.nih.gov/pubmed/33979407
http://dx.doi.org/10.1371/journal.pone.0251490
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