Advertisement

Pattern Recognition and Image Analysis

, Volume 25, Issue 2, pp 321–326 | Cite as

Classification of steady state visual evoked potentials by Multi-Class T-Weight Method

  • Z. Iscan
  • Z. Dokur
Applied Problems
  • 110 Downloads

Abstract

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.

Keywords

obtained classification performances applications of MCTW method 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    O. Friman, I. Volosyak, and A. Graser, “Multiple channel detection of steady-state visual evoked potentials for braincomputer interfaces,” IEEE Trans. Bio-Med. Eng. 54, 742–750 (2007).CrossRefGoogle Scholar
  2. 2.
    Z. Wu, Y. Lai, Y. Xia, D. Wu, and D. Yao, “Stimulator selection in SSVEP-based BCI,” Med. Eng. Phys. 30, 1079–1088 (2008).CrossRefGoogle Scholar
  3. 3.
    W. Yijun, G. Xiaorong, H. Bo, J. Chuan, and G. Shangkai, “Brain-computer interfaces based on visual evoked potentials,” IEEE Eng. Med. Biol. Mag. 27, 64–71 (2008).CrossRefGoogle Scholar
  4. 4.
    G. Xiaorong, X. Dingfeng, C. Ming, and G. Shangkai, “A BCI-based environmental controller for the motiondisabled,” IEEE Trans. Neural Syst. Rehabilit. Eng. 11, 137–140 (2003).CrossRefGoogle Scholar
  5. 5.
    B. Guangyu, G. Xiaorong, Y. Zheng, H. Bo, and G. Shangkai, “An online multichannel SSVEP-based brain–computer interface using a canonical correlation analysis method,” J. Neural Eng. 6, 046002 (2009).CrossRefGoogle Scholar
  6. 6.
    T. M. S. Mukesh, V. Jaganathan, and M. R. Reddy, “A novel multiple frequency stimulation method for steady state VEP based brain computer interfaces,” Physiol. Measur. 27, 61 (2006).CrossRefGoogle Scholar
  7. 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
  8. 8.
    R. Aler, I. M. Galvan, and J. M. Valls, “Evolving spatial and frequency selection filters for braincomputer interfaces,” in Proc. IEEE Congr. on Evolutionary Computation (CEC) (Barcelona, 2010), pp. 1–7.CrossRefGoogle Scholar
  9. 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. 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. 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. 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
  13. 13.
    F. Lotte, M. Congedo, A. Lecuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” J. Neural Eng. 4, R1–R13 (2007).CrossRefGoogle Scholar
  14. 14.
    K. Muller, C.W. Anderson, and G.E. Birch, “Linear and nonlinear methods for brain-computer interfaces,” IEEE Trans. Neural Syst. Rehabilit. Eng. 11, 165–169 (2003).CrossRefGoogle Scholar
  15. 15.
    M. Zhong, F. Lotte, M. Girolami, and A. Lecuyer, “Classifying EEG for brain computer interfaces using Gaussian processes,” Pattern Recogn. Lett. 29, 354–359 (2008).CrossRefGoogle Scholar
  16. 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. 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
  18. 18.
    V. Bostanov, “BCI Competition 2003–Data sets Ib and IIb: Feature extraction from eventrelated brain potentials with the continuous wavelet transform and the T-value scalogram,” IEEE Trans. Bio-Med. Eng. 51, 1057–1061 (2004).CrossRefGoogle Scholar
  19. 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

Copyright information

© Pleiades Publishing, Ltd. 2015

Authors and Affiliations

  1. 1.Department of Psychiatry and Behavioral SciencesStony Brook UniversityStony BrookUSA

Personalised recommendations