Validation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity

Abstract Neurofeedback training (NFT) enables users to learn self-control of EEG activity of interest and then to create many benefits on cognitive function. A considerable number of nonresponders who fail to achieve successful NFT have often been reported in the within-session prediction. This stud...

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Autores principales:	Ken-Hsien Su, Jen-Jui Hsueh, Tainsong Chen, Fu-Zen Shaw
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3f885e1e74da4662a79641a566068fe1
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spelling	oai:doaj.org-article:3f885e1e74da4662a79641a566068fe12021-12-02T18:37:09ZValidation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity10.1038/s41598-021-99235-72045-2322https://doaj.org/article/3f885e1e74da4662a79641a566068fe12021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99235-7https://doaj.org/toc/2045-2322Abstract Neurofeedback training (NFT) enables users to learn self-control of EEG activity of interest and then to create many benefits on cognitive function. A considerable number of nonresponders who fail to achieve successful NFT have often been reported in the within-session prediction. This study aimed to investigate successful EEG NFT of upregulation alpha activity in terms of trainability, independence, and between-session predictability validation. Forty-six participants completed 12 training sessions. Spectrotemporal analysis revealed the upregulation success on brain activity of 8–12 Hz exclusively to demonstrate trainability and independence of alpha NFT. Three learning indices of between-session changes exhibited significant correlations with eyes-closed resting state (ECRS) alpha amplitude before the training exclusively. Through a stepwise linear discriminant analysis, the prediction model of ECRS’s alpha frequency band amplitude exhibited the best accuracy (89.1%) validation regarding the learning index of increased alpha amplitude on average. This study performed a systematic analysis on NFT success, the performance of the 3 between-session learning indices, and the validation of ECRS alpha activity for responder prediction. The findings would assist researchers in obtaining insight into the training efficacy of individuals and then attempting to adapt an efficient strategy in NFT success.Ken-Hsien SuJen-Jui HsuehTainsong ChenFu-Zen ShawNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ken-Hsien Su Jen-Jui Hsueh Tainsong Chen Fu-Zen Shaw Validation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity
description	Abstract Neurofeedback training (NFT) enables users to learn self-control of EEG activity of interest and then to create many benefits on cognitive function. A considerable number of nonresponders who fail to achieve successful NFT have often been reported in the within-session prediction. This study aimed to investigate successful EEG NFT of upregulation alpha activity in terms of trainability, independence, and between-session predictability validation. Forty-six participants completed 12 training sessions. Spectrotemporal analysis revealed the upregulation success on brain activity of 8–12 Hz exclusively to demonstrate trainability and independence of alpha NFT. Three learning indices of between-session changes exhibited significant correlations with eyes-closed resting state (ECRS) alpha amplitude before the training exclusively. Through a stepwise linear discriminant analysis, the prediction model of ECRS’s alpha frequency band amplitude exhibited the best accuracy (89.1%) validation regarding the learning index of increased alpha amplitude on average. This study performed a systematic analysis on NFT success, the performance of the 3 between-session learning indices, and the validation of ECRS alpha activity for responder prediction. The findings would assist researchers in obtaining insight into the training efficacy of individuals and then attempting to adapt an efficient strategy in NFT success.
format	article
author	Ken-Hsien Su Jen-Jui Hsueh Tainsong Chen Fu-Zen Shaw
author_facet	Ken-Hsien Su Jen-Jui Hsueh Tainsong Chen Fu-Zen Shaw
author_sort	Ken-Hsien Su
title	Validation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity
title_short	Validation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity
title_full	Validation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity
title_fullStr	Validation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity
title_full_unstemmed	Validation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity
title_sort	validation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3f885e1e74da4662a79641a566068fe1
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Validation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity

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