Application of Semi-supervised Sparse Representation Classifier Based on Help Training in EEG Classification_Journal of Biomedical Engineering

Authors：

JIAMin ¹ ,  WANGJinjia ¹ , LIJing ^2,3 , HONGWenxue ³

1. School of Information Science and Engineer, Yanshan University, Qinhuangdao 066004, China;
2. School of Science, Yanshan University, Qinhuangdao 066004, China;
3. Institute of Biomedical Engineering, Yanshan University, Qinhuangdao 066004, China;

Corresponding author：

WANGJinjia, Email: wjj@ysu.edu.cn

Keywords：

semi-supervised learning; sparse representation classifier; help training; self-training; electroencephalogram; brain-computer interface

DOI：

10.7507/1001-5515.20140001

Video：

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Abstract Full text Figures/Tables Video References Cited by

Electroencephalogram (EEG) classification for brain-computer interface (BCI) is a new way of realizing human-computer interreaction. In this paper the application of semi-supervised sparse representation classifier algorithms based on help training to EEG classification for BCI is reported. Firstly, the correlation information of the unlabeled data is obtained by sparse representation classifier and some data with high correlation selected. Secondly, the boundary information of the selected data is produced by discriminative classifier, which is the Fisher linear classifier. The final unlabeled data with high confidence are selected by a criterion containing the information of distance and direction. We applied this novel method to the three benchmark datasets, which were BCIⅠ, BCIⅡ_Ⅳ and USPS. The classification rate were 97%,82% and 84.7%, respectively. Moreover the fastest arithmetic rate was just about 0.2 s. The classification rate and efficiency results of the novel method are both better than those of S3VM and SVM, proving that the proposed method is effective.

Citation： JIAMin, WANGJinjia, LIJing, HONGWenxue. Application of Semi-supervised Sparse Representation Classifier Based on Help Training in EEG Classification. Journal of Biomedical Engineering, 2014, 31(1): 1-6. doi: 10.7507/1001-5515.20140001 Copy

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7.	ZHOU Z H, LI M. Tri-training:exploiting unlabeled data using three classifiers[J]. IEEE Trans Knowl Data Eng, 2005, 17(11):1529-1541.
8.	ADANKON M M, CHERIET M, BIEM A. Semisupervised learning using bayesian interpretation:application to LS-SVM[J]. IEEE Trans Neural Netw, 2011, 22(4):513-524.
9.	王金甲,贾敏.标签均值的自训练半监督支持向量机用于EEG分类[J].中国生物医学工程学报,2011,30(5):666-672.
10.	ADANKON M M, CHERIET M. Help-training for semi-supervised support vector machines[J]. Pattern Recognit, 2011, 44(9):2220-2230.
11.	WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE Trans Pattern Anal Mach Intell, 2009, 31(2):210-227.
12.	BLANKERTZ B, MULLER K, CURIO G, et al. The BCI competition 2003:progress and perspectives in detection and discrimination of EEG single trials[J]. IEEE Trans Biomed Eng, 2004, 51(6):1044-1051.
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1. 杨平,刘铁军,尧德中.支持向量机后验概率方法在多任务脑机接口中的应用[J].中国生物医学工程学报,2009,28(2):171-176.
2. LI Y, GAO X R, LIU H S, et al. Classification of single-trial electroencephalogram during finger movement[J]. IEEE Trans Biomed Eng, 2004, 51(6):1019-1025.
3. LI Q Y, LI H Q, GUAN C T, et al. A self-training semi-supervised support vector machine algorithm and its applications in brain computer interface[C]//IEEE International Conference on Acoustics, Speech and Signal Processing, 2007. Honolulu, HI:2007, 1:385-388.
4. 周志华.半监督学习专刊前言[J].软件学报,2008,19(11):2789-2790.
5. 陈毅松,汪国平,董士海.基于支持向量机的渐进直推式分类学习算法[J].软件学报,2003,14(3):451-460.
6. BLUM A, MITCHELL T. Combining labeled and unlabeled data with co-training[C]//Proceeding of the 11th Annual Conference on Computational Learning Theory, Madison, WI:1998:92-100.
7. ZHOU Z H, LI M. Tri-training:exploiting unlabeled data using three classifiers[J]. IEEE Trans Knowl Data Eng, 2005, 17(11):1529-1541.
8. ADANKON M M, CHERIET M, BIEM A. Semisupervised learning using bayesian interpretation:application to LS-SVM[J]. IEEE Trans Neural Netw, 2011, 22(4):513-524.
9. 王金甲,贾敏.标签均值的自训练半监督支持向量机用于EEG分类[J].中国生物医学工程学报,2011,30(5):666-672.
10. ADANKON M M, CHERIET M. Help-training for semi-supervised support vector machines[J]. Pattern Recognit, 2011, 44(9):2220-2230.
11. WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE Trans Pattern Anal Mach Intell, 2009, 31(2):210-227.
12. BLANKERTZ B, MULLER K, CURIO G, et al. The BCI competition 2003:progress and perspectives in detection and discrimination of EEG single trials[J]. IEEE Trans Biomed Eng, 2004, 51(6):1044-1051.
13. Li Y F, KWOK J T, ZHOU Z.H. Semi-supervised learning using label mean[C]//Proceedings of the 26th Annual International Conference on Machine Learning. Montreal:2009:633-640.

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Application of Semi-supervised Sparse Representation Classifier Based on Help Training in EEG Classification

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