| 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
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| Source Publication | APPLIED SCIENCES-BASEL
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| 2021-12-01 | |
| Volume | 11Issue:23Pages:13 |
| Corresponding Author | Si, Juanning(sijuanning@bistu.edu.cn) ; Zhang, Yujin(yujinzhang@nlpr.ia.ac.cn) |
| Abstract | 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. |
| Keyword | 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 |
| WOS Keyword | CANONICAL CORRELATION-ANALYSIS ; BCI |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | Natural Science Foundation of Beijing, China ; National Natural Science Foundation of China ; Beijing Municipal Education Commission Science and Technology Program |
| WOS Research Area | Chemistry ; Engineering ; Materials Science ; Physics |
| WOS Subject | Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied |
| WOS ID | WOS:000742930100001 |
| Publisher | MDPI |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/47046 |
| Collection | 管理与支撑部门_重大项目处 |
| Corresponding Author | Si, Juanning; Zhang, Yujin |
| Affiliation | 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 |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences; Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation 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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