A Novel Channel Selection Method for Brain-computer Interface Based on Relief-SBS_Journal of Biomedical Engineering

Authors：

SHANHaijun ,  ZHUShan

College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;

Corresponding?author：

ZHUShan, Email: zsa@zju.edu.cn

Keywords：

brain-computer interface; motor imagery; channel selection; Relief-SBS

DOI：

10.7507/1001-5515.20160059

Video：

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Abstract Full text Figures/Tables Video References Cited by

Regarding to the channel selection problem during the classification of electroencephalogram (EEG) signals, we proposed a novel method, Relief-SBS, in this paper. Firstly, the proposed method performed EEG channel selection by combining the principles of Relief and sequential backward selection (SBS) algorithms. And then correlation coefficient was used for classification of EEG signals. The selected channels that achieved optimal classification accuracy were considered as optimal channels. The data recorded from motor imagery task experiments were analyzed, and the results showed that the channels selected with our proposed method achieved excellent classification accuracy, and also outperformed other feature selection methods. In addition, the distribution of the optimal channels was proved to be consistent with the neurophysiological knowledge. This demonstrates the effectiveness of our method. It can be well concluded that our proposed method, Relief-SBS, provides a new way for channel selection.

Citation： SHANHaijun, ZHUShan. A Novel Channel Selection Method for Brain-computer Interface Based on Relief-SBS. Journal of Biomedical Engineering, 2016, 33(2): 350-356. doi: 10.7507/1001-5515.20160059 Copy

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11.	MENG Jianjun, LIU Guangquan, HUANG Gan, et al. Automated selecting subset of channels based on CSP in motor imagery brain-computer interface system[C]//2009 IEEE International Conference on Rob Biomimetics. Guilin:2009:2290-2294.
12.	王金甲, 張玲智, 胡備.多類核共空間模式特征提取方法研究[J].生物醫學工程學雜志, 2012, 29(2):217-222.
13.	LAN Tian, ERDOGMUS D, ADAMI A, et al. Channel selection and feature projection for cognitive load estimation using ambulatory EEG[J]. Comput Intell Neurosci, 2007, 2007:74895.
14.	ROBNIK-SIKONJA M, KONONENKO I. Theoretical and empirical analysis of ReliefF and RReliefF[J]. Mach Learn, 2003, 53(1-2):23-69.
15.	REEVES S J, ZHE Zhao. Sequential algorithms for observation selection[J]. IEEE Transactions on Signal Processing, 1999, 47(1):123-132.
16.	OSMAN A, ROBERT A. Time-course of cortical activation during overt and imagined movements[C]//Proceeding of Cognitive Neuroscience Annual Meeting. New York, Mar. 2001:512-517.
17.	HJORTH B. An on-line transformation of EEG scalp potentials into orthogonal source derivations[J]. Electroencephalogr Clin Neurophysiol, 1975, 39(5):526-530.
18.	WANG Tao, DENG Jie, HE Bin. Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns[J]. Clin Neurophysiol, 2004, 115(12):2744-2753.
19.	ANTHONY M, HOLDEN S. Cross-validation for binary classification by real-valued functions:theoretical analysis[C]//Proceeding of Eleventh Annual Conference on Computational Learning Theory. New York:1999:218-229.
20.	SAJDA P, GERSON A, MüLLER K R, et al. A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces[J]//IEEE Trans Neural Sys Rehab Eng, 2003, 11(2):184-185.
21.	QIN Lei, DING Lei, HE Bin. Motor imagery classification by means of source analysis for brain-computer interface applications[J]. J Neural Eng, 2004, 1(3):135-141.

1. WOLPAW J R, BIRBAUMER N, MCFARLAND D J, et al. Brain-computer interfaces for communication and control[J]. Clin Neurophysiol, 2002, 113(6):767-791.
2. MüLLER-PUTZ G R, SCHERER R, PFURTSCHELLER G, et al. EEG-based neuroprosthesis control:a step towards clinical practice[J]. Neurosci Lett, 2005, 382(1-2):169-174.
3. YUAN Han, DOUD A, GURURAJAN A, et al. Cortical imaging of event-related (de)synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain[J]. IEEE Trans Neural Syst Rehabil Eng, 2008, 16(5):425-431.
4. FABIANI G E, MCFARLAND D J, WOLPAW J R, et al. Conversion of EEG activity into cursor movement by a brain-computer interface (BCI)[J]. IEEE Trans Neural Syst Rehabil Eng, 2004, 12(3):331-338.
5. YUAN Han, HE Bin. Brain-computer interfaces using sensorimotor rhythms:current state and future perspectives[J]. IEEE Trans Biomed Eng, 2014, 61(5):1425-1435.
6. PFURTSCHELLER G, LOPES DA SILVA F H. Event-related EEG/MEG synchronization and desynchronization:basic principles[J]. Clin Neurophysiol, 1999, 110(11):1842-1857.
7. MILLER K J, LEUTHARDT E C, SCHALK G, et al. Spectral changes in cortical surface potentials during motor movement[J]. J Neurosci, 2007, 27(9):2424-2432.
8. LAL T N, SCHR?DER M, HINTERBERGER T, et al. Support vector channel selection in BCI[J]. IEEE Trans Biomed Eng, 2004, 51(6):1003-1010.
9. ARVANEH M, GUAN C, ANG K K, et al. Optimizing the channel selection and classification accuracy in EEG-based BCI[J]. IEEE Trans Biomed Eng, 2011, 58(6):1865-1873.
10. WANG Yijun, GAO Shangkai, GAO Xiaorong. Common spatial pattern method for channel selelction in motor imagery based brain-computer interface[C]//27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. Shanghai:2005:5392-5395.
11. MENG Jianjun, LIU Guangquan, HUANG Gan, et al. Automated selecting subset of channels based on CSP in motor imagery brain-computer interface system[C]//2009 IEEE International Conference on Rob Biomimetics. Guilin:2009:2290-2294.
12. 王金甲, 張玲智, 胡備.多類核共空間模式特征提取方法研究[J].生物醫學工程學雜志, 2012, 29(2):217-222.
13. LAN Tian, ERDOGMUS D, ADAMI A, et al. Channel selection and feature projection for cognitive load estimation using ambulatory EEG[J]. Comput Intell Neurosci, 2007, 2007:74895.
14. ROBNIK-SIKONJA M, KONONENKO I. Theoretical and empirical analysis of ReliefF and RReliefF[J]. Mach Learn, 2003, 53(1-2):23-69.
15. REEVES S J, ZHE Zhao. Sequential algorithms for observation selection[J]. IEEE Transactions on Signal Processing, 1999, 47(1):123-132.
16. OSMAN A, ROBERT A. Time-course of cortical activation during overt and imagined movements[C]//Proceeding of Cognitive Neuroscience Annual Meeting. New York, Mar. 2001:512-517.
17. HJORTH B. An on-line transformation of EEG scalp potentials into orthogonal source derivations[J]. Electroencephalogr Clin Neurophysiol, 1975, 39(5):526-530.
18. WANG Tao, DENG Jie, HE Bin. Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns[J]. Clin Neurophysiol, 2004, 115(12):2744-2753.
19. ANTHONY M, HOLDEN S. Cross-validation for binary classification by real-valued functions:theoretical analysis[C]//Proceeding of Eleventh Annual Conference on Computational Learning Theory. New York:1999:218-229.
20. SAJDA P, GERSON A, MüLLER K R, et al. A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces[J]//IEEE Trans Neural Sys Rehab Eng, 2003, 11(2):184-185.
21. QIN Lei, DING Lei, HE Bin. Motor imagery classification by means of source analysis for brain-computer interface applications[J]. J Neural Eng, 2004, 1(3):135-141.

Journal of Biomedical Engineering

A Novel Channel Selection Method for Brain-computer Interface Based on Relief-SBS

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