Alpha band neurofeedback training based on a portable device improves working memory performance of young people. (February 2023)

• Record Type: Journal Article
• Authors: Chen, XiaoYu; Sui, Li
• Is Part Of: Biomedical signal processing and control. Volume 80(2023)Part 1
• Publication Date: 2023-02
• Subjects: Working memory -- Neurofeedback -- Electroencephalogram (EEG) -- Real-time EEG brain-computer interface -- Portable device; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104308
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: With the development of brain-computer interface and neurofeedback technology, future brain-computer interface devices will surely develop in the direction of practicality and portability. Based on this, we design and propose a low-cost, portable, and effective real-time EEG neurofeedback system. Portable real-time EEG neurofeedback system based on point 1. We designed a related alpha band adjusted EEG experiment for 16 days with a total of 8 training sessions. Subsequent analysis of the correlation results verifies the effectiveness of the system, which can change the corresponding alpha-band power to a certain extent. It can be preliminarily judged that the relative power adjustment of the alpha band affects working memory to a certain extent. Furthermore, we found that even neurofeedback training based on the alpha band of the prefrontal cortex resulted in increased power in other bands such as beta and gamma, as well as changes in the activity of related bands in the occipital cortex. Abstract: Background: Working Memory (WM) plays a key role in cognitive activities such as problem solving, intellectual reasoning, and mathematical calculation. It has been reported that electroencephalogram (EEG) based Neurofeedback (NF) training shows good application value in improving subjects' WM. Objectives: In this study, a low-cost and portable Neurofeedback Training (NFT) system based on a real-time EEG brain-computer interface (BCI) was built to explore whether the NFT based on alpha band can effectively regulate subjects' WM level. Methods: There were 16 healthy young subjects in total, recruited and randomly divided into the NFT group and the control group. Compared to the control group, the NFT group received a total of 8 NFT sessions, in which the subjects combined with the designed game feedback interface to realize the regulation of alpha band. Then, the relevant EEG data, the pre-test and post-test scores in the process of NF training were collected for EEG spectrum analysis and paired T-test respectively. Results: The study found that after a period of training, the alpha band power of prefrontal cortex and occipital cortex in the NFT group was significantly enhanced, and the activity power of beta and gamma band was significantly higher than that in other regions, and the WM error rate of this group was obviously lower than that of the control group (P < 0.001). Conclusion: Our study has demonstrated that the alpha band NFT system based on portable devices can improve the subjects' WM ability to a certain extent.