Toward consistency between humans and classifiers: Improved performance of a real-time brain–computer interface using a mutual learning system

Chun Yi Lin, Chia Feng Lu, Chi Wen Jao, Po Shan Wang, Yu Te Wu*

*此作品的通信作者

研究成果: Article同行評審

摘要

The performance of electroencephalography (EEG) classifiers in a brain–computer interface (BCI) depends heavily on the quality and consistency of training data. Therefore, facilitating collaboration between two independent systems, namely humans and machines, as well as increasing model generalizability and personalization are important tasks. In this study, we designed a mutual learning system to stabilize the EEG patterns of users who performed motor imagery (MI) and attention tasks, and we updated the parameters of a deep learning classifier in real time to improve consistency between the system and users. According to our results, the accuracy of the users on the MI task increased from 56% ± 13.9% to 81.5% ± 8.18%, and that on the attention task increased from 55% ± 7.07% to 82.5% ± 12.3% after application of the proposed mutual learning system. Thus, the mutual learning system facilitates the application of personalized BCIs and heralds a new era in which humans and machines can learn from each other.

原文English
文章編號120205
期刊Expert Systems with Applications
226
DOIs
出版狀態Published - 15 9月 2023

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