Classification of steady state visual evoked potentials by Multi-Class T-Weight Method
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In this paper, Multi-Class T-Weight Method (MCTW) is presented for classification in brain-computer interface (BCI) systems. Proposed method is an extension of the existing Improved T-Weight method for multi-class problems. The method was tested on the frequency and correlation based features obtained from electroencephalogram data of 20 Subjects in a steady state visual evoked potential (SSVEP) based offline BCI classification task. Obtained classification performances with different classifiers show that the MCTW method compete with the other well-known classifiers like linear discriminant analysis (LDA) and support vector machines (SVMs). Therefore, it can be used in classifying SSVEP based electroencephalogram data with proper features.
Keywordsobtained classification performances applications of MCTW method
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- 7.Y. Zheng, G. Xiaorong, B. Guangyu, H. Bo, and G. Shangkai, “A half-field stimulation pattern for SSVEP-based braincomputer interface,” in Proc. Annu. Int. Conf. of the IEEE Engineering in Medicine and Biology Society (Mineeapolis, 2009), pp. 6461–6464.Google Scholar
- 9.T. Yamaguchi, K. Omori, J. Irie, and K. Inoue, “Feature extraction from EEG signals in SSVEP spelling system,” in Proc. SICE Annu. Conf. (Taipei, 2010), pp. 58–62.Google Scholar
- 10.Z. Zimu, L. Xiuquan, and D. Zhidong, “A CWT-based SSVEP classification method for brain-computer interface system,” in Proc. Int. Conf. on Intelligent Control and Information Processing (ICICIP) (Dalian, 2010), pp. 43–48.Google Scholar
- 11.Z. Li, Y. Pengxian, X. Longteng, M. Qingguo, H. Daofu, and S. Hui, “Research on SSVEP feature extraction based on HHT,” in Proc. 7th Int. Conf. on Fuzzy Systems and Knowledge Discovery (FSKD) (Yantai, 2010), pp. 2220–2223.Google Scholar
- 12.E. A. Felton, “Human factors studies of brain-computer interfaces: performance and mental effort for able and physically disabled,” PhD (Univ. of Wisconsin- Madison, 2007).Google Scholar
- 16.Z. Iscan and Z. Dokur, “Improved T-Weight method in classification of slow cortical potentials,” in Proc. 4th Int. Symp. on Applied Sciences in Biomedical and Communication Technologies (Barcelona, 2011), pp. 1–5.Google Scholar
- 17.L. Yang, Z. Zongtan, H. Dewen, and D. Guohua, “T-weighted approach for neural information processing in P300 based brain-computer interface,” in Proc. Int. Conf. Neural Networks and Brain (Beijing, 2005), pp. 1535–1539.Google Scholar
- 19.R. P. W. Duin, P. Juszczak, D. Ridder, P. Paclik, E. Pekal-ska, and D. M. J. Tax, PRTools: A Matlab Toolbox for Pattern Recognition (Delft Univ. of Technology, 2007).Google Scholar