A supervised independent component analysis algorithm for motion imagery-based brain computer interface. (May 2022)

• Record Type: Journal Article
• Authors: Zou, Yijun; Zhao, Xingang; Chu, Yaqi; Xu, Weiliang; Han, Jianda; Li, Wei
• Is Part Of: Biomedical signal processing and control. Volume 75(2022)
• Publication Date: 2022-05
• Subjects: Brain-computer interface (BCI) -- Electroencephalogram (EEG) -- Machine learning -- Movement imagination -- Independent component analysis; 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.2022.103576
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Recognizing the corresponding neural activities of independent components(ICs) obtained by independent component analysis(ICA) is of prime importance to take use of ICA in EEG analysis. There are many methods trying to solve this problem. But most of them combining ICA, a unsupervised method, and recognition of ICs in a separate way. In this paper, we propose a supervised method to extract the independent components corresponding to different motion imagery(MI) activities in the brain. By designing a new optimization objective and solving it, we combine the idea of ICA with principle of MI in an individual algorithm. From the perspective of event-related desynchronization and synchronization (ERD/ERS), specific frequency band power of the motion-related component should be enhanced or suppressed when executing or imaging movement of body. Therefore, the new optimization function extract the components that satisfy both independence and band power maximization for specific motions. Then, we solve this optimization problem based on the fixed-point iteration scheme. In the experimental stages, we show that our methods can extract motion-related independent components without losing independence. Experimental results show that, although basing on the principle of ERD/ERS, our methods' effectiveness can be verified in the perspective of movement-related potential (MRP). Additionally, by identifying features in the extracted motion-related independent components, we can achieve better motion recognition accuracy. When using the proposed algorithms with different schema, the results yielded significant accuracy imporvements of 6.9%(p < 0.001) and 7.9%(p < 0.01).