A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition_Journal of Biomedical Engineering

Authors：

 WANGJinjia , LIUYuan

College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China;

Corresponding author：

WANGJinjia, Email: wjj@ysu.edu.cn

Keywords：

brain computer interface; electrocorticogram; magnetoencephalogram; multivariate empirical mode decomposition; intrinsic mode function; power feature

DOI：

10.7507/1001-5515.20150081

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This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competitionⅢand competitionⅣreached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

Citation： WANGJinjia, LIUYuan. A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition. Journal of Biomedical Engineering, 2015, 32(2): 451-454, 464. doi: 10.7507/1001-5515.20150081 Copy

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.	HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc R Soc Lond A, 1998, 454(1971):903-995.
3.	PANOULAS K I, HADJILEONTIADIS L J, PANAS S M. Hilbert-Huang Spectrum as a new field for the identification of EEG event related de-/synchronization for BCI applications[C]//30th Annual International Conference of the IEEE, Vancouver:2008:3832-3835.
4.	王金甲, 周丽娜, 赵玉超.基于MEG的脑机接口特征提取方法研究[J].仪器仪表学报, 2010, 31(7):1460-1465.
5.	LOONEY D, MANDIC D P. Multiscale image fusion using complex extensions of EMD[J]., IEEE Trans Signal Proces, 2009, 57(4):1626-1630.
6.	TANAKA T, MANDIC D P. Complex empirical mode decomposition[J].IEEE Signal Proces Lett, 2007, 14(2):101-104.
7.	RILLING G, FLANDRIN P, GONALVES P, et al. Bivariate empirical mode decomposition[J]. IEEE Signal Proces Lett, 2007, 14(12):936-939.
8.	REHMAN N, MANDIC D P. Empirical mode decomposition for trivariate signals[J]. IEEE Trans Signal Proces, 2010, 58(3):1059-1068.
9.	REHMAN N, MANDIC D P. Multivariate empirical mode decomposition[J]. Proc R Soc A:Math, Phys Eng Sci, 2010, 466(2117):1291-1302.
10.	HUANG N E, WU M L C, LONG S R, et al. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis[J]. Proc R Soc Lond A:Math, Phys Eng Sci, 2003, 459(2037):2317-2345.
11.	Cannegieter S C, Rosendaal F R, Briet E. Thromboembolic and bleeding complications in patients with mechanical heart valve prostheses[J]. Circulation, 1994, 89(2):635-641.
12.	王金甲, 尹涛.基于CSSD-DE通道选择算法的ECoG分类研究.中国生物医学工程学报, 2012, 31(5):712-719.
13.	PARK C, LOONEY D, REHMAN N, et al. Classification of motor imagery BCI using multivariate empirical mode decomposition[J]. IEEE Trans Neur Systems Rehabilit Eng, 2013, 21(1):10-22.

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. HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc R Soc Lond A, 1998, 454(1971):903-995.
3. PANOULAS K I, HADJILEONTIADIS L J, PANAS S M. Hilbert-Huang Spectrum as a new field for the identification of EEG event related de-/synchronization for BCI applications[C]//30th Annual International Conference of the IEEE, Vancouver:2008:3832-3835.
4. 王金甲, 周丽娜, 赵玉超.基于MEG的脑机接口特征提取方法研究[J].仪器仪表学报, 2010, 31(7):1460-1465.
5. LOONEY D, MANDIC D P. Multiscale image fusion using complex extensions of EMD[J]., IEEE Trans Signal Proces, 2009, 57(4):1626-1630.
6. TANAKA T, MANDIC D P. Complex empirical mode decomposition[J].IEEE Signal Proces Lett, 2007, 14(2):101-104.
7. RILLING G, FLANDRIN P, GONALVES P, et al. Bivariate empirical mode decomposition[J]. IEEE Signal Proces Lett, 2007, 14(12):936-939.
8. REHMAN N, MANDIC D P. Empirical mode decomposition for trivariate signals[J]. IEEE Trans Signal Proces, 2010, 58(3):1059-1068.
9. REHMAN N, MANDIC D P. Multivariate empirical mode decomposition[J]. Proc R Soc A:Math, Phys Eng Sci, 2010, 466(2117):1291-1302.
10. HUANG N E, WU M L C, LONG S R, et al. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis[J]. Proc R Soc Lond A:Math, Phys Eng Sci, 2003, 459(2037):2317-2345.
11. Cannegieter S C, Rosendaal F R, Briet E. Thromboembolic and bleeding complications in patients with mechanical heart valve prostheses[J]. Circulation, 1994, 89(2):635-641.
12. 王金甲, 尹涛.基于CSSD-DE通道选择算法的ECoG分类研究.中国生物医学工程学报, 2012, 31(5):712-719.
13. PARK C, LOONEY D, REHMAN N, et al. Classification of motor imagery BCI using multivariate empirical mode decomposition[J]. IEEE Trans Neur Systems Rehabilit Eng, 2013, 21(1):10-22.

Journal of Biomedical Engineering

A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition

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