Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

doi:10.3390/app112311453

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	Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM
	Gao, Yuhang 3; Si, Juanning 3; Wu, Sijin 3; Li, Weixian 3; Liu, Hao 1,2; Chen, Jianhu 3; He, Qing 3; Zhang, Yujin 1,2
刊名	APPLIED SCIENCES-BASEL
	2021-12-01
卷号	11 期号:23 页码:13
关键词	steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) l1-regularized multiway canonical correlation analysis (L1-MCCA) support vector machine (SVM) particle swarm optimization (PSO)
DOI	10.3390/app112311453
通讯作者	Si, Juanning(sijuanning@bistu.edu.cn) ; Zhang, Yujin(yujinzhang@nlpr.ia.ac.cn)
英文摘要	Canonical correlation analysis (CCA) has been used for the steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) for a long time. However, the reference signal of CCA is relatively simple and lacks subject-specific information. Moreover, over-fitting may occur when a short time window (TW) length was used in CCA. In this article, an optimized L1-regularized multiway canonical correlation analysis (L1-MCCA) is combined with a support vector machine (SVM) to overcome the aforementioned shortcomings in CCA. The correlation coefficients obtained by L1-MCCA were transferred into a particle-swarm-optimization (PSO)-optimized support vector machine (SVM) classifier to improve the classification accuracy. The performance of the proposed method was evaluated and compared with the traditional CCA and power spectral density (PSD) methods. The results showed that the accuracy of the L1-MCCA-PSO-SVM was 96.36% and 98.18% respectively when the TW lengths were 2 s and 6 s. This accuracy is higher than that of the traditional CCA and PSD methods.
资助项目	Natural Science Foundation of Beijing, China[4214080] ; National Natural Science Foundation of China[81871398] ; Beijing Municipal Education Commission Science and Technology Program[KM202011232008] ; Beijing Municipal Education Commission Science and Technology Program[KM201911232019]
WOS关键词	CANONICAL CORRELATION-ANALYSIS ; BCI
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
语种	英语
出版者	MDPI
WOS记录号	WOS:000742930100001
资助机构	Natural Science Foundation of Beijing, China ; National Natural Science Foundation of China ; Beijing Municipal Education Commission Science and Technology Program
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47045]
专题	自动化研究所_管理与支撑部门_重大项目处
通讯作者	Si, Juanning; Zhang, Yujin
作者单位	1.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Beijing Informat Sci & Technol Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100192, Peoples R China
推荐引用方式 GB/T 7714	Gao, Yuhang,Si, Juanning,Wu, Sijin,et al. Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM[J]. APPLIED SCIENCES-BASEL,2021,11(23):13.
APA	Gao, Yuhang.,Si, Juanning.,Wu, Sijin.,Li, Weixian.,Liu, Hao.,...&Zhang, Yujin.(2021).Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM.APPLIED SCIENCES-BASEL,11(23),13.
MLA	Gao, Yuhang,et al."Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM".APPLIED SCIENCES-BASEL 11.23(2021):13.