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Comparison of Feature Extraction Methods for EEG BCI Classification

  • Tomas UktverisEmail author
  • Vacius Jusas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 538)

Abstract

This work analyzes several feature extraction methods used in today’s EEG BCI (electro-encephalogram brain computer interface) classification systems. Comparison of multiple EEG energy algorithms is presented for solving a 4-class motor imagery BCI classification problem. Furthermore, multiple feature vector generation techniques are employed into analysis. The effectiveness of CSP (common spatial pattern) filtering method in preprocessing step is shown. Channel difference feature extraction method is presented. It is discussed that key aim in today’s EEG signal analysis should be dedicated to finding more accurate techniques for determining better quality features. Initial tests prove that static feature extraction methods are not optimal and adaptive algorithms are required to overcome subject specific EEG signal variations. Further work and new dynamic feature extraction methods are required to solve the problem.

Keywords

Common spatial patterns Brain-computer interface Laplace filtering Feature extraction Channel difference 

References

  1. 1.
    Brodu, N., et al.: Comparative study of band-power extraction techniques for motor imagery classification. In: IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), pp. 1–6 (2011)Google Scholar
  2. 2.
    Pfurtscheller, G., et al.: Graz-Brain-Computer Interface: State of Research, pp. 65–84. MIT Press, Cambridge (2007)Google Scholar
  3. 3.
    Kaiser, JF.: On a simple algorithm to calculate the energy of a signal. In: IEEE International Conference on Acoustic Speech Signal Process, Albuquerque, NM (1990)Google Scholar
  4. 4.
    Martišius, I., et al.: Using higher order nonlinear operators for SVM classification of EEG data. Elektronika ir Elektrotechnika 119(3), 99–102 (2012)CrossRefGoogle Scholar
  5. 5.
    Dolezal, J., Cerny, V., Stastny, J.: Online motor-imagery based BCI. In: International Conference on Applied Electronics (AE), pp. 65–68, 5–7 (2012)Google Scholar
  6. 6.
    Tandonnet, C., Burle, B., Hasbroucq, T., Vidal, F.: Spatial enhancement of EEG traces by surface Laplacian estimation: comparison between local and global methods. Clin. Neurophysiol. 116, 18–24 (2005)CrossRefGoogle Scholar
  7. 7.
    Qin, L., He, B.: A wavelet-based time-frequency analysis approach for classification of motor imagery for brain-computer interface applications. J. Neural Eng. 2, 65–72 (2005)CrossRefGoogle Scholar
  8. 8.
    Müller-Gerking, J., et al.: Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin. Neurophysiol. 110(5), 787–798 (1999)CrossRefGoogle Scholar
  9. 9.
    Thang, L.Q., Temiyasathit, C.: Increase performance of four-class classification for motor-imagery based brain-computer interface. In: 2014 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 1–5, 7–9 (2014)Google Scholar
  10. 10.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)CrossRefGoogle Scholar
  11. 11.
    Ben-Hur, A., Weston, J.: A user’s guide to support vector machines. In: Carugo, O., Eisenhaber, F. (eds.) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol. 609, pp. 223–239. Humana Press, New York (2010)CrossRefGoogle Scholar
  12. 12.
    Szachewicz, P.: Classification of Motor Imagery for Brain-Computer Interfaces. Master’s thesis, Poznan University of Technology, Poznan (2013)Google Scholar
  13. 13.
    Hsu, C.-W., et al.: A Practical Guide to Support Vector Classification. National Taiwan University, Taiwan (2010)Google Scholar
  14. 14.
    Brunner, C., et al.: BCI Competition 2008 – Graz data set A (2008). https://www.bbci.de/competition/iv/desc_2a.pdf
  15. 15.
    Schlogl, A., et al.: Evaluation criteria in BCI research. In: Dornhege, G., del Millan, J.R., Hinterberger, T., McFarland, D.J., Muller, K.-R. (eds.) Toward Brain-Computer Interfacing, pp. 327–342. MIT Press, Cambridge (2007)Google Scholar
  16. 16.
    Ang, K.K., et al.: Filter bank common spatial pattern (FBCSP) in brain-computer interface. In: Neural Networks, IJCNN 2008. IEEE World Congress on Computational Intelligence, pp. 2390–2397, 1–8 June 2008Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Software Engineering DepartmentKaunas University of TechnologyKaunasLithuania

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