Online brain-computer interface system based on independent component analysis_Journal of Biomedical Engineering

Authors：

HU Pan ^1,2 , ZHANG Lei ^1,2 , ZHOU Bangyan ^1,2 ,  WU Xiaopei ^1,2

1. School of Computer Science and Technology, Anhui University, Hefei 230601, P.R.China;
2. The Key Lab. of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230039, P.R.China;

Corresponding author：

WU Xiaopei, Email: wxp2001@ahu.edu.cn

Keywords：

independent component analysis; brain-computer interface; online system; motor imagery

DOI：

10.7507/1001-5515.201603003

Video：

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Abstract Full text Figures/Tables Video References Cited by

In the research of non-invasive brain-computer interface (BCI), independent component analysis (ICA) has been considered as a promising method of electroencephalogram (EEG) preprocessing and feature enhancement. However, there have been few investigations and implements about online ICA-BCI system up till now. This paper reports the investigation of the ICA-based motor imagery BCI (MIBCI) system, combining the characteristics of unsupervised learning of ICA and event-related desynchronization (ERD) related to motor imagery. We constructed a simple and practical method of ICA spatial filter calculation and discriminate criterion of three-type motor imageries in the study. To validate the online performance of proposed algorithms, an ICA-MIBCI experimental system was fully established based on NeuroScan EEG amplifier and VC++ platform. Four subjects participated in the experiment of MIBCI testing and two of them took part in the online experiment. The average classification accuracies of the three-type motor imageries reached 89.78% and 89.89% in the offline and online testing, respectively. The experimental results showed that the proposed algorithm produced high classification accuracy and required less time consumption, which would have a prospect of cross platform application.

Citation： HU Pan, ZHANG Lei, ZHOU Bangyan, WU Xiaopei. Online brain-computer interface system based on independent component analysis. Journal of Biomedical Engineering, 2017, 34(1): 106-114. doi: 10.7507/1001-5515.201603003 Copy

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9.	段放, 高小榕. 一种基于运动想象的脑-机接口时空滤波器迭代算法. 中国生物医学工程学报, 2011, 30(1): 11-16.
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11.	郭晓静, 吴小培, 张道信, 等. 基于独立分量分析的脑电消噪与特征提取. 系统仿真学报, 2003, 15(2): 287-289.
12.	Brunner C, Naeem M, Leeb R, et al. Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis. Pattern Recognit Lett, 2007, 28(8): 957-964.
13.	Grosse-Wentrup M, Buss M. Multiclass common spatial patterns and information theoretic feature extraction. IEEE Trans Biomed Eng, 2008, 55(8): 1991-2000.
14.	Wang Yijun, Wang Yute, Jung T P. Translation of EEG spatial filters from resting to motor imagery using Independent component analysis. PLoS One, 2012, 7(5): e37665.
15.	Lotte F, Guan C. Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng, 2011, 58(2): 355-362.
16.	Lang S, Kotchoubey B. Learning effects on event-related brain potentials. Neuroreport, 2000, 11(15): 3327-3331.
17.	Kubota M, Sakakihara Y, Uchiyama Y, et al. New ocular movement detector system as a communication tool in ventilator-assisted Werdnig-Hoffmann disease. Dev Med Child Neurol, 2000, 42(1): 61-64.
18.	Wu W, Chen Z, Gao X, et al. Probabilistic common spatial patterns for multichannel EEG analysis. IEEE Trans Pattern Anal Mach Intell, 2015, 37(3): 639-653.
19.	Kang H, Choi S. Bayesian common spatial patterns for multi-subject EEG classification. Neural Netw, 2014, 57(9): 39-50.
20.	Samek W, Meinecke F C, Müller K R. Transferring subspaces between subjects in brain——computer interfacing. IEEE Trans Biomed Eng, 2013, 60(8): 2289-2298.
21.	Samek W, Kawanabe M, Müller K R. Divergence-based framework for common spatial patterns algorithms. IEEE Rev Biomed Eng, 2014, 7: 50-72.
22.	Delorme A, Palmer J, Onton J, et al. Independent EEG sources are dipolar. PLoS One, 2012, 7(2): e30135.

1. Pfurtscheller G, Flotzinger D, Kalcher J. Brain computer-interface-a new communication device for handicapped persons. Journal of Microcomputer Applications, 1993, 16(3): 293-299.
2. He B, Baxter B, Edelman B J, et al. Noninvasive brain-computer interfaces based on sensorimotor rhythms. Proceedings of the IEEE, 2015, 103(6): 907-925.
3. Vaughan T M, Wolpaw J R. The Third International Meeting on Brain-Computer interface technology: making a difference. IEEE Trans Neural Syst Rehabil Eng, 2006, 14(2): 126-127.
4. Makeig S, Kothe C, Mullen T, et al. Evolving signal processing for brain-computer interfaces. Proceedings of the IEEE, 2012(100): 1567-1584.
5. 程明, 高上凯, 张琳. 基于脑电信号的脑-计算机接口. 北京生物医学工程, 2000, 19(2): 113-118.
6. 吴小培, 周蚌艳, 张磊, 等. 运动想象脑-机接口中的 ICA 滤波器设计. 生物物理学报, 2014, 30(7): 540-554.
7. Ang K K, Chin Z Y, Wang C, et al. Filter Bank Common Spatial Pattern Algorithm on BCI Competition ⅣDatasets 2a and 2b. Front Neurosci, 2012, 6: 39.
8. Blankertz B, Tomioka R, Lemm S, et al. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag, 2008, 25(1): 41-56.
9. 段放, 高小榕. 一种基于运动想象的脑-机接口时空滤波器迭代算法. 中国生物医学工程学报, 2011, 30(1): 11-16.
10. 杨福生, 洪波. 独立分量分析的原理与应用. 北京: 清华大学出版社, 2006.
11. 郭晓静, 吴小培, 张道信, 等. 基于独立分量分析的脑电消噪与特征提取. 系统仿真学报, 2003, 15(2): 287-289.
12. Brunner C, Naeem M, Leeb R, et al. Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis. Pattern Recognit Lett, 2007, 28(8): 957-964.
13. Grosse-Wentrup M, Buss M. Multiclass common spatial patterns and information theoretic feature extraction. IEEE Trans Biomed Eng, 2008, 55(8): 1991-2000.
14. Wang Yijun, Wang Yute, Jung T P. Translation of EEG spatial filters from resting to motor imagery using Independent component analysis. PLoS One, 2012, 7(5): e37665.
15. Lotte F, Guan C. Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng, 2011, 58(2): 355-362.
16. Lang S, Kotchoubey B. Learning effects on event-related brain potentials. Neuroreport, 2000, 11(15): 3327-3331.
17. Kubota M, Sakakihara Y, Uchiyama Y, et al. New ocular movement detector system as a communication tool in ventilator-assisted Werdnig-Hoffmann disease. Dev Med Child Neurol, 2000, 42(1): 61-64.
18. Wu W, Chen Z, Gao X, et al. Probabilistic common spatial patterns for multichannel EEG analysis. IEEE Trans Pattern Anal Mach Intell, 2015, 37(3): 639-653.
19. Kang H, Choi S. Bayesian common spatial patterns for multi-subject EEG classification. Neural Netw, 2014, 57(9): 39-50.
20. Samek W, Meinecke F C, Müller K R. Transferring subspaces between subjects in brain——computer interfacing. IEEE Trans Biomed Eng, 2013, 60(8): 2289-2298.
21. Samek W, Kawanabe M, Müller K R. Divergence-based framework for common spatial patterns algorithms. IEEE Rev Biomed Eng, 2014, 7: 50-72.
22. Delorme A, Palmer J, Onton J, et al. Independent EEG sources are dipolar. PLoS One, 2012, 7(2): e30135.

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