Channel Selection for Multi-class Motor Imagery Based on Common Spatial Pattern_Journal of Biomedical Engineering

Authors：

ZHOUBangyan ,  WUXiaopei , LUZhao , ZHANGLei , GUOXiaojing , ZHANGChao

The Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China;

Corresponding?author：

WUXiaopei, Email: wxp2001@ahu.edu.cn

Keywords：

brain-computer interface; electroencephalogram; common spatial pattern; motor imagery

DOI：

10.7507/1001-5515.20150095

Video：

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Abstract Full text Figures/Tables Video References Cited by

High-density channels are often used to acquire electroencephalogram (EEG) spatial information in different cortical regions of the brain in brain-computer interface (BCI) systems. However, applying excessive channels is inconvenient for signal acquisition, and it may bring artifacts. To avoid these defects, the common spatial pattern (CSP) algorithm was used for channel selection and a selection criteria based on norm-2 is proposed in this paper. The channels with the highest M scores were selected for the purpose of using fewer channels to acquire similar rate with high density channels. The DatasetⅢa from BCI competition 2005 were used for comparing the classification accuracies of three motor imagery between whole channels and the selected channels with the present proposed method. The experimental results showed that the classification accuracies of three subjects using the 20 channels selected with the present method were all higher than the classification accuracies using all 60 channels, which convinced that our method could be more effective and useful.

Citation： ZHOUBangyan, WUXiaopei, LUZhao, ZHANGLei, GUOXiaojing, ZHANGChao. Channel Selection for Multi-class Motor Imagery Based on Common Spatial Pattern. Journal of Biomedical Engineering, 2015, 32(3): 520-525. doi: 10.7507/1001-5515.20150095 Copy

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11.	HE Lin, HU Youpan, LI Yuanqing, et al. Channel selection by Rayleigh coeffient maximization based genetic algorithm for classifying single-trial motor imagery EEG[J]. Neurocomputing, 2013, 121:423-433.
12.	LAN T, ERDOGMUS D, ADAMI A, et al. Salient EEG channel selection in brain computer interfaces by mutual information maximization[C]//27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. Shanghai:2005:7064-7067.
13.	MENG Jianjun, LIU Guangquan, HUANG G, et al. Automated selecting subset of channels based on CSP in motor imagery brain-computer interface system[C]//2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). Guilin, China:2009:2290-2294.
14.	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.
15.	KIM J Y, PARK S M, KO K E, et al. Optimal EEG channel selection for motor imagery BCI system using BPSO and GA[M]//KIM J H, MATSON E T, MYUNG H, et al. Robot Intelligence Technology and Applications 2012. Berlin Heidelberg:Springer Berlin Heidelberg, 2013:231-239.
16.	WU Wei, GAO Xiaorong, GAO Shangkai. One-Versus-the-Rest (OVR) algorithm:an extension of common spatial patterns (CSP) algorithm to multi-class case[C]//27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. Shanghai:2005:2387-2390.
17.	YONG Xinyi, WARD R K, BIRCH G E. Sparse spatial filter optimization for EEG channel reduction in brain-computer interface[C]//IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. Las Vegas, NV:2008:417-420.

1. ?NICOLAS-ALONSO L F, GOMEZ-GIL J. Brain computer interfaces, a review[J]. Sensors (Basel), 2012, 12(2):1211-1279.
2. HULBERT S, ADELI H. EEG/MEG-and imaging-based diagnosis of Alzheimer's disease[J]. Rev Neurosci, 2013, 24(6):563-576.
3. HERWIG U, SATRAPI P, SCH? NFELDT-LECUONA C. Using the international 10-20 EEG system for positioning of transcranial magnetic stimulation[J]. Brain Topogr, 2003, 16(2):95-99.
4. CRUSE D, CHENNU S, FERNáNDEZ-ESPEJO D, et al. Detecting awareness in the vegetative state:electroencephalographic evidence for attempted movements to command[J]. PLoS One, 2012, 7(11):e49933.
5. GROSSE-WENTRUP M, BUSS M. Multiclass common spatial patterns and information theoretic feature extraction[J]. IEEE Trans Biomed Eng, 2008, 55(8):1991-2000.
6. ARVANEH M, GUAN Cuntai, 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.
7. POPESCU F, FAZLI S, BADOWER Y, et al. Single trial classification of motor imagination using 6 dry EEG electrodes[J]. PLoS One, 2007, 2(7):e637.
8. AL MOUBAYED N, HASAN B A, GAN J Q, et al. Continuous presentation for multi-objective channel selection in Brain-Computer Interfaces[C]//2012 IEEE Congress on Evolutionary Computation (CEC). Brisbane, QLD:2012:1-7.
9. 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.
10. SCHR?DER M, LAL T N, HINTERBERGER T, et al. Robust EEG channel selection across subjects for brain-computer interfaces[J]. EURASIP J Appl Signal Process, 2005, 19:3103-3112.
11. HE Lin, HU Youpan, LI Yuanqing, et al. Channel selection by Rayleigh coeffient maximization based genetic algorithm for classifying single-trial motor imagery EEG[J]. Neurocomputing, 2013, 121:423-433.
12. LAN T, ERDOGMUS D, ADAMI A, et al. Salient EEG channel selection in brain computer interfaces by mutual information maximization[C]//27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. Shanghai:2005:7064-7067.
13. MENG Jianjun, LIU Guangquan, HUANG G, et al. Automated selecting subset of channels based on CSP in motor imagery brain-computer interface system[C]//2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). Guilin, China:2009:2290-2294.
14. 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.
15. KIM J Y, PARK S M, KO K E, et al. Optimal EEG channel selection for motor imagery BCI system using BPSO and GA[M]//KIM J H, MATSON E T, MYUNG H, et al. Robot Intelligence Technology and Applications 2012. Berlin Heidelberg:Springer Berlin Heidelberg, 2013:231-239.
16. WU Wei, GAO Xiaorong, GAO Shangkai. One-Versus-the-Rest (OVR) algorithm:an extension of common spatial patterns (CSP) algorithm to multi-class case[C]//27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. Shanghai:2005:2387-2390.
17. YONG Xinyi, WARD R K, BIRCH G E. Sparse spatial filter optimization for EEG channel reduction in brain-computer interface[C]//IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. Las Vegas, NV:2008:417-420.

Journal of Biomedical Engineering

Channel Selection for Multi-class Motor Imagery Based on Common Spatial Pattern

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