EEG microstate features according to performance on a mental arithmetic task

Abstract In this study, we hypothesized that task performance could be evaluated applying EEG microstate to mental arithmetic task. This pilot study also aimed at evaluating the efficacy of microstates as novel features to discriminate task performance. Thirty-six subjects were divided into good and...

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Autores principales:	Kyungwon Kim, Nguyen Thanh Duc, Min Choi, Boreom Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/09cf5a5c198e40bd970156b81a4e1071
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id	oai:doaj.org-article:09cf5a5c198e40bd970156b81a4e1071
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spelling	oai:doaj.org-article:09cf5a5c198e40bd970156b81a4e10712021-12-02T15:22:57ZEEG microstate features according to performance on a mental arithmetic task10.1038/s41598-020-79423-72045-2322https://doaj.org/article/09cf5a5c198e40bd970156b81a4e10712021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79423-7https://doaj.org/toc/2045-2322Abstract In this study, we hypothesized that task performance could be evaluated applying EEG microstate to mental arithmetic task. This pilot study also aimed at evaluating the efficacy of microstates as novel features to discriminate task performance. Thirty-six subjects were divided into good and poor performers, depending on how well they performed the task. Microstate features were derived from EEG recordings during resting and task states. In the good performers, there was a decrease in type C and an increase in type D features during the task compared to the resting state. Mean duration and occurrence decreased and increased, respectively. In the poor performers, occurrence of type D feature, mean duration and occurrence showed greater changes. We investigated whether microstate features were suitable for task performance classification and eleven features including four archetypes were selected by recursive feature elimination (RFE). The model that implemented them showed the highest classification performance for differentiating between groups. Our pilot findings showed that the highest mean Area Under Curve (AUC) was 0.831. This study is the first to apply EEG microstate features to specific cognitive tasks in healthy subjects, suggesting that EEG microstate features can reflect task achievement.Kyungwon KimNguyen Thanh DucMin ChoiBoreom LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kyungwon Kim Nguyen Thanh Duc Min Choi Boreom Lee EEG microstate features according to performance on a mental arithmetic task
description	Abstract In this study, we hypothesized that task performance could be evaluated applying EEG microstate to mental arithmetic task. This pilot study also aimed at evaluating the efficacy of microstates as novel features to discriminate task performance. Thirty-six subjects were divided into good and poor performers, depending on how well they performed the task. Microstate features were derived from EEG recordings during resting and task states. In the good performers, there was a decrease in type C and an increase in type D features during the task compared to the resting state. Mean duration and occurrence decreased and increased, respectively. In the poor performers, occurrence of type D feature, mean duration and occurrence showed greater changes. We investigated whether microstate features were suitable for task performance classification and eleven features including four archetypes were selected by recursive feature elimination (RFE). The model that implemented them showed the highest classification performance for differentiating between groups. Our pilot findings showed that the highest mean Area Under Curve (AUC) was 0.831. This study is the first to apply EEG microstate features to specific cognitive tasks in healthy subjects, suggesting that EEG microstate features can reflect task achievement.
format	article
author	Kyungwon Kim Nguyen Thanh Duc Min Choi Boreom Lee
author_facet	Kyungwon Kim Nguyen Thanh Duc Min Choi Boreom Lee
author_sort	Kyungwon Kim
title	EEG microstate features according to performance on a mental arithmetic task
title_short	EEG microstate features according to performance on a mental arithmetic task
title_full	EEG microstate features according to performance on a mental arithmetic task
title_fullStr	EEG microstate features according to performance on a mental arithmetic task
title_full_unstemmed	EEG microstate features according to performance on a mental arithmetic task
title_sort	eeg microstate features according to performance on a mental arithmetic task
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/09cf5a5c198e40bd970156b81a4e1071
work_keys_str_mv	AT kyungwonkim eegmicrostatefeaturesaccordingtoperformanceonamentalarithmetictask AT nguyenthanhduc eegmicrostatefeaturesaccordingtoperformanceonamentalarithmetictask AT minchoi eegmicrostatefeaturesaccordingtoperformanceonamentalarithmetictask AT boreomlee eegmicrostatefeaturesaccordingtoperformanceonamentalarithmetictask
_version_	1718387366876413952

EEG microstate features according to performance on a mental arithmetic task

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