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Bidirectional Siamese correlation analysis method for enhancing the detection of SSVEPs

文献类型：期刊论文

作者Zhang, Xinyi4,5; Qiu, Shuang5; Zhang, Yukun5; Wang, Kangning3; Wang, Yijun1,2; He, Huiguang1,4,5
刊名JOURNAL OF NEURAL ENGINEERING
出版日期2022-08-01
卷号19期号:4页码:20
ISSN号1741-2560
关键词brain-computer interface (BCI) electroencephalogram (EEG) deep learning steady-state visual evoked potential (SSVEP)
DOI10.1088/1741-2552/ac823e
通讯作者He, Huiguang(huiguang.he@ia.ac.cn)
英文摘要Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have attracted increasing attention due to their high information transfer rate. To improve the performance of SSVEP detection, we propose a bidirectional Siamese correlation analysis (bi-SiamCA) model. Approach. In this model, an long short-term memory-based Siamese architecture is designed to measure the similarity between the SSVEP signal and the template in each frequency and obtain the probability that the SSVEP signal belongs to each frequency. Additionally, a maximize agreement module with a designed contrastive loss is adopted in the Siamese architecture to increase the similarity between the SSVEP signal and the reference signal in the same frequency. Moreover, a two-way signal processing mechanism is built to effectively integrate complementary information from two temporal directions of the input signals. Our model uses raw SSVEPs as inputs and can be trained end-to-end. Main results. Experimental results on a 40-class dataset and a 12-class dataset indicate that bi-SiamCA can significantly improve the classification accuracy compared with the prominent traditional and deep learning methods, especially under short data lengths. Feature visualizations show that the similarity between the SSVEP signal and the reference signal in the same frequency gradually improved in our model. Conclusion. The proposed bi-SiamCA model enhances the performance of SSVEP detection and outperforms the compared methods. Significance. Due to its high decoding accuracy under short signals, our approach has great potential to implement a high-speed SSVEP-based BCI.
WOS关键词CANONICAL CORRELATION-ANALYSIS ; BRAIN-COMPUTER INTERFACES ; RECOGNITION
资助项目National Natural Science Foundation of China[62020106015] ; National Natural Science Foundation of China[61976209] ; National Natural Science Foundation of China[U21A20388] ; CAS International Collaboration Key Project[173211KYSB20190024] ; Strategic Priority Research Program of CAS[XDB32040000] ; Beijing Science and Technology Program[J210010]
WOS研究方向Engineering ; Neurosciences & Neurology
语种英语
出版者IOP Publishing Ltd
WOS记录号WOS:000834589000001
资助机构National Natural Science Foundation of China ; CAS International Collaboration Key Project ; Strategic Priority Research Program of CAS ; Beijing Science and Technology Program
源URL[http://ir.ia.ac.cn/handle/173211/49845]  
专题类脑智能研究中心_神经计算及脑机交互
通讯作者He, Huiguang
作者单位1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Semiconductors, State Key Lab Integrated Optoelect, Beijing, Peoples R China
3.Tianjin Univ, Acad Med Engn & Translat Med, Tianjin, Peoples R China
4.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Natl Lab Pattern Recognit, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Xinyi,Qiu, Shuang,Zhang, Yukun,et al. Bidirectional Siamese correlation analysis method for enhancing the detection of SSVEPs[J]. JOURNAL OF NEURAL ENGINEERING,2022,19(4):20.
APA Zhang, Xinyi,Qiu, Shuang,Zhang, Yukun,Wang, Kangning,Wang, Yijun,&He, Huiguang.(2022).Bidirectional Siamese correlation analysis method for enhancing the detection of SSVEPs.JOURNAL OF NEURAL ENGINEERING,19(4),20.
MLA Zhang, Xinyi,et al."Bidirectional Siamese correlation analysis method for enhancing the detection of SSVEPs".JOURNAL OF NEURAL ENGINEERING 19.4(2022):20.

入库方式： OAI收割

来源：自动化研究所

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