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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, Yuhang1; Si, Juanning1; Wu, Sijin1; Li, Weixian1; Liu, Hao2,3; Chen, Jianhu1; He, Qing1; Zhang, Yujin2,3
发表期刊APPLIED SCIENCES-BASEL
2021-12-01
卷号11期号:23页码:13
通讯作者Si, Juanning(sijuanning@bistu.edu.cn) ; Zhang, Yujin(yujinzhang@nlpr.ia.ac.cn)
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.
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)
DOI10.3390/app112311453
关键词[WOS]CANONICAL CORRELATION-ANALYSIS ; BCI
收录类别SCI
语种英语
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]
项目资助者Natural Science Foundation of Beijing, China ; National Natural Science Foundation of China ; Beijing Municipal Education Commission Science and Technology Program
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
WOS类目Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号WOS:000742930100001
出版者MDPI
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/47046
专题管理与支撑部门_重大项目处
通讯作者Si, Juanning; Zhang, Yujin
作者单位1.Beijing Informat Sci & Technol Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100192, Peoples R China
2.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
通讯作者单位中国科学院自动化研究所;  模式识别国家重点实验室
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.
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