Bidirectional Siamese correlation analysis method for enhancing the detection of SSVEPs

doi:10.1088/1741-2552/ac823e

CORC > 自动化研究所 > 中国科学院自动化研究所 > 类脑智能研究中心 > 神经计算及脑机交互

	Bidirectional Siamese correlation analysis method for enhancing the detection of SSVEPs
	Zhang, Xinyi 4,5; Qiu, Shuang 5; Zhang, Yukun 5; Wang, Kangning 3; Wang, Yijun 1,2; He, Huiguang 1,4,5
刊名	JOURNAL OF NEURAL ENGINEERING
	2022-08-01
卷号	19 期号:4 页码:20
关键词	brain-computer interface (BCI) electroencephalogram (EEG) deep learning steady-state visual evoked potential (SSVEP)
ISSN号	1741-2560
DOI	10.1088/1741-2552/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.
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关键词	CANONICAL CORRELATION-ANALYSIS ; BRAIN-COMPUTER INTERFACES ; RECOGNITION
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
内容类型	期刊论文
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.