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A New Method of EEG Classification for BCI with Feature Extraction Based on Higher Order Statistics of Wavelet Components and Selection with Genetic Algorithms

  • Marcin Kołodziej
  • Andrzej Majkowski
  • Remigiusz J. Rak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)

Abstract

A new method of feature extraction and selection of EEG signal for brain-computer interface design is presented. The proposed feature selection method is based on higher order statistics (HOS) calculated for the details of discrete wavelets transform (DWT) of EEG signal. Then a genetic algorithm is used for feature selection. During the experiment classification is conducted on a single trial of EEG signals. The proposed novel method of feature extraction using HOS and DWT gives more accurate results then the algorithm based on discrete Fourier transform (DFT).

Keywords

feature extraction feature selection genetic algorithms (GA) higher order statistics (HOS) discrete wavelet transform (DWT) brain-computer interface (BCI) data-mining 

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References

  1. 1.
    Vidal, J.J.: Direct brain-computer communication. Ann. Rev. Biophys Bioeng. 2 (1973)Google Scholar
  2. 2.
    Molina, G.: Direct Brain-Computer Communication through scalp recorded EEG signals. PhD Thesis, École Polytechnique Fédérale de Lausane (2004)Google Scholar
  3. 3.
    Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., Mcfarland, D.J., Hunter Peckham, P., Schalk, G., Donchin, E., Quatrano, L.A., Robinson, C.J., Vaughan, T.M.: Brain–Computer Interface Technology: A Review ofthe First International Meeting. IEEE Transactions on Rehabilitation Engineering 8(2) (June 2000)Google Scholar
  4. 4.
    Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. IEEE Press & John Wiley (November 2002)Google Scholar
  5. 5.
    Documentation of Genetic Algorithm and Direct Search ToolboxTM - MATLABGoogle Scholar
  6. 6.
    Kołodziej, M., Majkowski, A., Rak, R.J.: A new method of feature extraction from EEG signal for braincomputer interface design. Przeglad Elektrotechniczny 86(9), 35–38 (2010)Google Scholar
  7. 7.
    Kołodziej, M., Majkowski, A., Rak, R.J.: Matlab FE-Toolbox - An universal utility for feature extraction of EEG signals for BCI realization. Przeglad Elektrotechniczny 86(1), 44–46Google Scholar
  8. 8.
    Kołodziej, M., Majkowski, A., Rak, R.J.: Implementation of genetic algorithms to feature selection for the use of brain-computer interface. In: CPEE 2010 (2010)Google Scholar
  9. 9.
    del Millán, J.R.: On the need for on-line learning in brain-computer interfaces. In: Proc. Int. Joint Conf. on Neural Networks (2004)Google Scholar
  10. 10.
    Mallat, S.: A wavelet Tour of Signal Processing. Academic Press, London (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marcin Kołodziej
    • 1
  • Andrzej Majkowski
    • 1
  • Remigiusz J. Rak
    • 1
  1. 1.Warsaw University of TechnologyWarsawPoland

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