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Working memory training using EEG neurofeedback in normal young adults

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 3rd International Conference on Biomedical Engineering and Biotechnology, 25–28 September 2014, Beijing, China

Article type: Research Article

Authors: Xiong, Shi | Cheng, Chen | Wu, Xia | Guo, Xiaojuan | Yao, Li | Zhang, Jiacai;

Affiliations: College of Information Science and Technology, Beijing Normal University, Beijing 100875, China | State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China

Note: [] Corresponding author: Jiacai Zhang, College of Information Science and Technology, Beijing Normal University, No. 19, Xinjiekouwai St., Haidian District, Beijing 100875, China. Tel.: +86 10 58807856; Fax: +86 10 58800056; E-mail: [email protected].

Keywords: Electroencephalography, neurofeedback, working memory, power spectrum, self-regulation

DOI: 10.3233/BME-141191

Journal: Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 3637-3644, 2014

Published: 2014
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Abstract

Recent studies have shown that working memory (WM) performance can be improved by intensive and adaptive computerized training. Here, we explored the WM training effect using Electroencephalography (EEG) neurofeedback (NF) in normal young adults. In the first study, we identified the EEG features related to WM in normal young adults. The receiver operating characteristic (ROC) curve showed that the power ratio of the theta-to-alpha rhythms in the anterior-parietal region, accurately classified a high percentage of the EEG trials recorded during WM and fixation control (FC) tasks. Based on these results, a second study aimed to assess the training effects of the theta-to-alpha ratio and tested the hypothesis that up-regulating the power ratio can improve working memory behavior. Our results demonstrated that these normal young adults succeeded in improving their WM performance with EEG NF, and the pre- and post-test evaluations also indicated that WM performance increase in experimental group was significantly greater than control groups. In summary, our findings provided preliminarily evidence that WM performance can be improved through learned regulation of the EEG power ratio using EEG NF.

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