Study on Electroencephalogram Recognition Framework by Common Spatial Pattern and Fuzzy Fusion_Journal of Biomedical Engineering

Authors：

 XULuqiang ^1,2 , XIAOGuangcan ² , LIMaofeng ²

1. Information School, Southwest Jiaotong University, Chengdu 610031, China;
2. Computer School, Southwest University of Science and Technology, Mianyang 621010, China;

Corresponding author：

XULuqiang, Email: 752511022@qq.com

Keywords：

electroencephalogram; common spatial patterns; information fusion; brain computer interface; linear discriminant analysis; Choquet; fuzzy integral

DOI：

10.7507/1001-5515.20150208

Video：

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Abstract Full text Figures/Tables Video References Cited by

Common spatial pattern (CSP) is a very popular method for spatial filtering to extract the features from electroencephalogram (EEG) signals, but it may cause serious over-fitting issue. In this paper, after the extraction and recognition of feature, we present a new way in which the recognition results are fused to overcome the over-fitting and improve recognition accuracy. And then a new framework for EEG recognition is proposed by using CSP to extract features from EEG signals, using linear discriminant analysis (LDA) classifiers to identify the user's mental state from such features, and using Choquet fuzzy integral to fuse classifiers results. Brain-computer interface (BCI) competition 2005 data setsⅣa was used to validate the framework. The results demonstrated that it effectively improved recognition and to some extent overcome the over-fitting problem of CSP. It showed the effectiveness of this framework for dealing with EEG.

Citation： XULuqiang, XIAOGuangcan, LIMaofeng. Study on Electroencephalogram Recognition Framework by Common Spatial Pattern and Fuzzy Fusion. Journal of Biomedical Engineering, 2015, 32(6): 1173-1178. doi: 10.7507/1001-5515.20150208 Copy

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13.	ZHANG H, YANG H, GUAN C. Bayesian learning for spatial filtering in an EEG-based brain-computer interface[J]. IEEE Trans Neural Netw Learn Syst, 2013, 24(7):1049-1060.
14.	王熙照.模糊测度和模糊积分及在分类技术中的应用[M].北京:科学出版社, 2008:18-19.
15.	AUEPHANWIRIYAKUL S, KELLER J M, GADER P D. Generalized Choquet fuzzy integral fusion[J]. Information fusion, 2002, 3(1):69-85.
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1. JEON Y, NAM C S, KIM Y J, et al. Event-related (De)synchronization (ERD/ERS) during motor imagery tasks:Implication for brain-computer interfaces[J]. I Int J Industr Ergonom, 2011, 41(5):428-436.
2. NICOLAS-ALONSO L F, GOMEZ-GIL J. Brain computer interfaces, a review[J]. Sensors (Basel), 2012, 12(2):1211-1279.
3. LEMM S, BLANKERTZ B, DICKHAUS T, et al. Introduction to machine learning for brain imaging[J]. NeuroImage, 2011, 56(2):387-399.
4. BLANKERTZ B, SANNELLI C, HALDER S, et al. Neurophysiological predictor of SMR-based BCI performance[J]. NeuroImage, 2010, 51(4):1303-1309.
5. SANNELLI C, BRAUN M, MULLER K R. Improving BCI performance by task-related trial pruning[J]. Neural Netw, 2009, 22(9):1295-1304.
6. ARVANEH M, GUAN C, ANG K K, et al. Optimizing spatial filters by minimizing within-class dissimilarities in electroencephalogram-based brain-computer interface[J]. IEEE Trans Neural Netw Learn Syst, 2013, 24(4):610-619.
7. SAMEK W, KAWANABE M, MULLER K R. Divergence-based framework for common spatial patterns algorithms[J]. IEEE Trans Biomed Eng, 2014, 7:50-72.
8. LU H, ENG H L, GUAN C, et al. Regularized common spatial pattern with aggregation for EEG classification in small-sample setting[J]. IEEE Trans Biomed Eng, 2010, 57(12):2936-2946.
9. KAWANABE M, SAMEK W, MULLER K R, et al. Robust common spatial filters with a maxmin approach[J]. Neural Comput, 2014, 26(2):1-28.
10. WANG H, TANG Q, ZHENG W. Ll-norm-based common spatial patterns[J]. IEEE Trans Biomed Eng, 2012, 59(3):653-662.
11. PARK J, CHUNG W. Common spatial patterns based on generalized norms[C]//2013 International Winter Workshop on Brain-Computer Interface (BCI). Gangwo:2013:39-42.
12. MOUSAVI E A, MALLER J J, FITZGERALD P B, et al. Wavelet common spatial pattern in asynchronous offline brain computer interfaces[J]. Biomed Signal Process Control, 2011, 6(2):121-128.
13. ZHANG H, YANG H, GUAN C. Bayesian learning for spatial filtering in an EEG-based brain-computer interface[J]. IEEE Trans Neural Netw Learn Syst, 2013, 24(7):1049-1060.
14. 王熙照.模糊测度和模糊积分及在分类技术中的应用[M].北京:科学出版社, 2008:18-19.
15. AUEPHANWIRIYAKUL S, KELLER J M, GADER P D. Generalized Choquet fuzzy integral fusion[J]. Information fusion, 2002, 3(1):69-85.
16. SCHALK G, BLANKERTZ B, SCHLOGL A, et al. BCI CompetitionⅢ[EB/OL]. (2005-06-16)[2015-03-07].http://www.bbci.de/competition/iii/.

Journal of Biomedical Engineering

Study on Electroencephalogram Recognition Framework by Common Spatial Pattern and Fuzzy Fusion

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