CASIA OpenIR  > 脑图谱与类脑智能实验室  > 神经计算与脑机交互
A CNN-based compare network for classification of SSVEPs in human walking
Wu, Chenyao1,2; Qiu, Shuang1; Xing, Jiezhen1,2; He, Huiguang1,2,3
2020-07
Conference Name2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Conference Date20-24 July 2020
Conference PlaceMontreal, QC, Canada
PublisherIEEE
Abstract

Brain-computer interface (BCI) can provide a way for the disabled to interact with the outside world. Steady-state visual evoked potential (SSVEP), which evokes potential through visual stimulation is one of important BCI paradigms. In laboratory environment, the classification accuracy of SSVEPs is excellent. However, in motion state, the accuracy will be greatly affected and reduce quite a lot. In this paper, in order to improve the classification accuracy of the SSVEP signals in the motion state, we collected SSVEP data of five targets at three speeds of 0km/h, 2.5km/h and 5km/h. A compare network based on convolutional neural network (CNN) was proposed to learn the relationship between EEG signal and the template corresponding to each stimulus frequency and classify. Compared with traditional methods (i.e., CCA, FBCCA and SVM) and state-of-the-art method (CNN) on the collected SSVEP datasets of 20 subjects, the method we proposed always performed best at different speeds. Therefore, these results validated the effectiveness of the method. In addition, compared with the speed of 0 km / h, the accuracy of the compare network at a high walking rate (5km/h) did not decrease much, and it could still maintain a good performance.

DOI10.1109/EMBC44109.2020.9176649
Indexed ByEI
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48823
Collection脑图谱与类脑智能实验室_神经计算与脑机交互
Corresponding AuthorHe, Huiguang
Affiliation1.Research Center for Brain-inspired Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wu, Chenyao,Qiu, Shuang,Xing, Jiezhen,et al. A CNN-based compare network for classification of SSVEPs in human walking[C]:IEEE,2020.
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