A supervised independent component analysis algorithm for motion imagery-based brain computer interface
文献类型:期刊论文
作者 | Zou YJ(邹宜君)1,3; Zhao XG(赵新刚)1; Chu YQ(褚亚奇)1,4; Xu WL(徐卫良)1; Han JD(韩建达)1; Li, Wei2 |
刊名 | Biomedical Signal Processing and Control |
出版日期 | 2022 |
卷号 | 75页码:1-8 |
ISSN号 | 1746-8094 |
关键词 | Brain-computer interface (BCI) Electroencephalogram (EEG) Independent component analysis Machine learning Movement imagination |
产权排序 | 1 |
英文摘要 | 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 |
语种 | 英语 |
WOS记录号 | WOS:000783256000008 |
资助机构 | National Natural Science Foundation of China (U1813214, 61773369, 61573340) |
源URL | [http://ir.sia.cn/handle/173321/30523] |
专题 | 沈阳自动化研究所_机器人学研究室 |
通讯作者 | Zhao XG(赵新刚) |
作者单位 | 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, China 2.Department of Computer Science, University of Liverpool, UK, Liverpool, United Kingdom 3.School of Internet Fiance and Information Engineering, GuangDong University of Finance, Guangzhou, China 4.University of Chinese Academy of Science, Beijing, China |
推荐引用方式 GB/T 7714 | Zou YJ,Zhao XG,Chu YQ,et al. A supervised independent component analysis algorithm for motion imagery-based brain computer interface[J]. Biomedical Signal Processing and Control,2022,75:1-8. |
APA | Zou YJ,Zhao XG,Chu YQ,Xu WL,Han JD,&Li, Wei.(2022).A supervised independent component analysis algorithm for motion imagery-based brain computer interface.Biomedical Signal Processing and Control,75,1-8. |
MLA | Zou YJ,et al."A supervised independent component analysis algorithm for motion imagery-based brain computer interface".Biomedical Signal Processing and Control 75(2022):1-8. |
入库方式: OAI收割
来源:沈阳自动化研究所
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