Classifying Electroencephalogram Signal Using Under-determined Blind Source Separation and Common Spatial Pattern_Journal of Biomedical Engineering

Authors：

 LUHuibin

School of Information Science and Engineer, Yanshan University, Qinhuangdao 066004, China;

Corresponding author：

LUHuibin, Email: yjsbl@ysu.edu.cn

Keywords：

brain computer interface; motor imagery; electroencephalogram; underdetermined blind source separation; common spatial pattern

DOI：

10.7507/1001-5515.20160038

Video：

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Abstract Full text Figures/Tables Video References Cited by

One of the key problems of brain-computer interfaces (BCI) is low signal-to-noise ratio (SNR) of electroencephalogram (EEG) signals. It affects recognition performance. To remove the artifact and noise, block under-determined blind source separation method based on the small number of channels is proposed in this paper. The non-stationary EEG signals are turned into block stationary signals by piecewise. The mixing matrix is estimated by the second-order under-determined blind mixing matrix identification. Then, the beamformer based on minimum mean square error separates the original sources of signals. Eventually, the reconstructed EEG for mixed signals removes the unwanted components of source signals to achieve suppressing artifact. The experiment results on the real motor imagery BCI indicated that the block under-determined blind source separation method could reconstruct signals and remove artifact effectively. The accuracy of motor imagery task of BCI has been greatly improved.

Citation： LUHuibin. Classifying Electroencephalogram Signal Using Under-determined Blind Source Separation and Common Spatial Pattern. Journal of Biomedical Engineering, 2016, 33(2): 216-220. doi: 10.7507/1001-5515.20160038 Copy

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1. MAEDER C L, SANNELLI C, HAUFE S A. Pre-stimulus sensorimotor rhythms influence brain-computer interface classification performance[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012, 20(5):653-662.
2. 王金甲, 张玲智, 胡备.多类核共空间模式特征提取方法研究[J].生物医学工程学杂志, 2012, 29(2):217-222.
3. 马峰, 张宁, 戴礼荣.基于语音信号稀疏性的FDICA初始化和后处理方法[J].数据采集与处理, 2012, 27(2):210-217.
4. LAHAT D, CARDOSO J, MESSER H. Second-order multidimensional ICA:performance analysis[J]. IEEE Trans Signal Process, 2012, 60(9):4598-4610.
5. DOMANOV I, DE LATHAUWER L. Blind channel identification of MISO systems based on the CP decomposition of cumulant tensors[C]//201119th European Signal Processing Conference. Barcelona:2011:2215-2218.
6. DE LATHAUWER L, CASTAING J. Blind identification of underdetermined mixtures by simultaneous matrix diagonalization[J]. IEEE Trans Signal Process, 2008, 56(3):1096-1105.
7. TICHAVSKY P, YEREDOR A. Fast approximate joint diagonalization incorporating weight matrices[J]. IEEE Trans Signal Process, 2009, 57(3):878-891.
8. DE LATHAUWER L. A link between the canonical decomposition in multilinear algebra and simultaneous matrix diagonalization[J]. SIAM J Matrix Anal Appl, 2006, 28(3):642-666.
9. KIM S G, YOO C D. Underdetermined blind source separation based on subspace representation[J]. IEEE Trans Signal Process, 2009, 57(7):2604-2614.
10. DE LATHAUWER L, CASTAING J. Second-order blind identification of underdetermined mixtures[C]//6th International Conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006. Berlin Heidelberg:Springer, 2006:40-47.
11. PAATERO P. A weighted non-negative least squares algorithm for three-way 'PARAFAC' factor analysis[J]. Chemometrics and Intelligent Laboratory Systems, 1997, 38(2):223-242.
12. YEREDOR A. On blind channel identification and equalization over Galois fields[C]//2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Florence:2014:4239-4243.
13. PARK C, LOONEY D, REHMAN N U, et al. Classification of motor imagery BCI using multivariate empirical mode decomposition[J]. IEEE Trans Neur Syst Rehabil Eng, 2013, 21(1):10-22.

Journal of Biomedical Engineering

Classifying Electroencephalogram Signal Using Under-determined Blind Source Separation and Common Spatial Pattern

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