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

研究成果: Article同行評審

摘要

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.

原文English
文章編號19615
期刊Scientific reports
11
發行號1
DOIs
出版狀態Published - 2021 十二月

All Science Journal Classification (ASJC) codes

  • 多學科

指紋

深入研究「Validation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity」主題。共同形成了獨特的指紋。

引用此