Research on the Methods for Electroencephalogram Feature Extraction Based on Blind Source Separation_Journal of Biomedical Engineering

Authors：

 WANGJiang ¹ , ZHANGHuiyuan ¹ , WANGLei ² , XUGuizhi ²

1. Tangshan Vocation and Technical College, Tangshan 063000, China;
2. The Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin 300130, China;

Corresponding author：

WANGJiang, Email: tswxwj@yeah.net

Keywords：

brain computer interface; blind source separation; independent component analysis

DOI：

10.7507/1001-5515.20140227

Video：

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Abstract Full text Figures/Tables Video References Cited by

In the present investigation, we studied four methods of blind source separation/independent component analysis (BSS/ICA), AMUSE, SOBI, JADE, and FastICA. We did the feature extraction of electroencephalogram (EEG) signals of brain computer interface (BCI) for classifying spontaneous mental activities, which contained four mental tasks including imagination of left hand, right hand, foot and tongue movement. Different methods of extract physiological components were studied and achieved good performance. Then, three combined methods of SOBI and FastICA for extraction of EEG features of motor imagery were proposed. The results showed that combining of SOBI and ICA could not only reduce various artifacts and noise but also localize useful source and improve accuracy of BCI. It would improve further study of physiological mechanisms of motor imagery.

Citation： WANGJiang, ZHANGHuiyuan, WANGLei, XUGuizhi. Research on the Methods for Electroencephalogram Feature Extraction Based on Blind Source Separation. Journal of Biomedical Engineering, 2014, 31(6): 1195-1201. doi: 10.7507/1001-5515.20140227 Copy

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2.	李明爱,王蕊,郝冬梅.想象左右手运动的脑电特征提取及分类研究[J].中国生物医学工程学报, 2009, 28(2):166-170.
3.	唐艳,柳建新,龚安栋,等.ICA + CSSD的脑-机接口分类[J].电子科技大学学报, 2008, 37(3):467-469.
4.	GOUY-PAILLER C, CONGEDO M. Non-stationary brain source separation for multi-class motor imagery[J]. IEEE Trans Biomed Engin, 2010, 57(2):469-478.
5.	JAMES C J, WANG S. Blind source separation in single-channel eeg analysis:an application to BCI[C]//Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS'06). New York, USA:2006:6544-6547.
6.	HERAULT J, JUTTEN C. Space or time adaptive signal processing by neural network models[C]//AIP Conference Proceedings. New York, USA:1986:206-211.
7.	COMON P. Independent component analysis, A new concept?[J]. Signal Processing, 1994, 36(3):287-314.
8.	BELL A J, SEJNOWSKI T J. An information-maximization approach to blind separation and blind deconvolution[J]. Neural Comput, 1995, 7(6):1129-1159.
9.	AMARI S I. New learning in structural parameter space-natural Riemannian gradient[J]. Advances in Neural Information Processing Systems, 1997, 9:127-133.
10.	HYVÄRINEN A. Fast and robust fixed-point algorithm for independent component analysis[J]. IEEE Trans Neural Netw, 1999, 10(3):626-634.
11.	LANG T, SOON V C, HUANG Y F, et al. AMUSE:a new blind identification algorithm[C]//IEEE International Symposium on Circuits and Systems. New Orleans, LA, USA:1990:1784-1787.
12.	BELOUCHRANI A, ABED-MERAIM K, CARDOSO J F, et al. A blind source separation technique using second-order statistics[J]. IEEE Trans Signal Processing, 1997, 45(2):434-444.
13.	CARDOSO J F, SOULOUMIAC A. Blind beamforming for non-Gaussian signals[J]. IEE Proceedings F Radar and Signal Processing, 1993,140(6):362-370.
14.	WANG J, XU G Z, WANG L, et al. Classifying EEG for brain-computer interface using spatio-temporal filters[C]//ISICA. Wuhan, China:2008:184-187.

1. WOLPAW J R, BIRBAUMER N, MCFARLAND D J, et al. Brian-computer interfaces for communication and control[J]. Clin Neurophysiol, 2002, 113(6):767-791.
2. 李明爱,王蕊,郝冬梅.想象左右手运动的脑电特征提取及分类研究[J].中国生物医学工程学报, 2009, 28(2):166-170.
3. 唐艳,柳建新,龚安栋,等.ICA + CSSD的脑-机接口分类[J].电子科技大学学报, 2008, 37(3):467-469.
4. GOUY-PAILLER C, CONGEDO M. Non-stationary brain source separation for multi-class motor imagery[J]. IEEE Trans Biomed Engin, 2010, 57(2):469-478.
5. JAMES C J, WANG S. Blind source separation in single-channel eeg analysis:an application to BCI[C]//Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS'06). New York, USA:2006:6544-6547.
6. HERAULT J, JUTTEN C. Space or time adaptive signal processing by neural network models[C]//AIP Conference Proceedings. New York, USA:1986:206-211.
7. COMON P. Independent component analysis, A new concept?[J]. Signal Processing, 1994, 36(3):287-314.
8. BELL A J, SEJNOWSKI T J. An information-maximization approach to blind separation and blind deconvolution[J]. Neural Comput, 1995, 7(6):1129-1159.
9. AMARI S I. New learning in structural parameter space-natural Riemannian gradient[J]. Advances in Neural Information Processing Systems, 1997, 9:127-133.
10. HYVÄRINEN A. Fast and robust fixed-point algorithm for independent component analysis[J]. IEEE Trans Neural Netw, 1999, 10(3):626-634.
11. LANG T, SOON V C, HUANG Y F, et al. AMUSE:a new blind identification algorithm[C]//IEEE International Symposium on Circuits and Systems. New Orleans, LA, USA:1990:1784-1787.
12. BELOUCHRANI A, ABED-MERAIM K, CARDOSO J F, et al. A blind source separation technique using second-order statistics[J]. IEEE Trans Signal Processing, 1997, 45(2):434-444.
13. CARDOSO J F, SOULOUMIAC A. Blind beamforming for non-Gaussian signals[J]. IEE Proceedings F Radar and Signal Processing, 1993,140(6):362-370.
14. WANG J, XU G Z, WANG L, et al. Classifying EEG for brain-computer interface using spatio-temporal filters[C]//ISICA. Wuhan, China:2008:184-187.

Journal of Biomedical Engineering

Research on the Methods for Electroencephalogram Feature Extraction Based on Blind Source Separation

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