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Study of discriminant analysis applied to motor imagery bipolar data

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Abstract

We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain–computer interface research. Differences in classification accuracy of the classifiers were found when using different amounts of data; if a small amount was available, the best classifier was linear discriminant analysis (LDA) and if enough data were available all three classifiers performed very similar. This suggests that the effort needed to find regularizing parameters for RDA can be avoided by using LDA.

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References

  1. Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kübler A, Perelmouter J, Taub E, Flor H (1999) A brain-controlled spelling device for the completely paralyzed. Nature 398:297–298

    Article  Google Scholar 

  2. Blankertz B, Curio G, Müller KR (2002) Classifying single trial EEG: towards brain computer interfacing. In: Diettrich TG, Becker S, Ghahramani Z (eds) Advances in neural information processing systems 14. MIT Press, Cambridge, pp 157–164

    Google Scholar 

  3. Millán J del R, Mourino J (2003) Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project. IEEE Trans Neural Syst Rehabil Eng 11(2):159–161

    Article  Google Scholar 

  4. Duda O, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley Interscience, New York

    MATH  Google Scholar 

  5. Friedman JH (1989) Regularized discriminant analysis. J Am Stat Assoc 84(405):165–175

    Article  Google Scholar 

  6. Garrett D, Peterson DA, Anderson CW, Thaut MH (2003) Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans Neural Syst Rehabil Eng 11(2):141–144

    Article  Google Scholar 

  7. Guger C, Schlögl A, Neuper C, Walterspacher C, Strein D, Pfurtscheller T, Pfurtscheller G (2001) Rapid prototyping of an EEG-based brain–computer interface (BCI). IEEE Trans Neural Syst Rehabil Eng 9:49–58

    Article  Google Scholar 

  8. Haselsteiner E, Pfurtscheller G (2000) Using time-dependent neural networks for EEG classification. IEEE Trans Rehabil Eng 8(4):457–463

    Article  Google Scholar 

  9. Krausz G, Scherer R, Korisek G, Pfurtscheller G (2003) Critical decision-speed and information transfer in the ‘graz brain–computer interface’. Appl Psychophysiol Biofeedback 28:233–240

    Article  Google Scholar 

  10. Mika S, Raetsch G, Weston J, Schölkopf B, Müller KR (1999) Fisher discriminant analysis with kernels. In: Hu YH, Larsen J, Wilson E, Douglas S (eds) Neural Networks for Signal Processing IX. IEEE, Piscataway, pp 41–48

  11. Müller KR, Anderson CW, Birch GE (2003) Linear and non-linear methods for brain–computer interfaces. IEEE Trans Neural Syst Rehabil Eng 11:165–169

    Article  Google Scholar 

  12. Neuper C, Schlögl A, Pfurtscheller G (2000) Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery. J Clin Neurophysiol 16:373–382

    Article  Google Scholar 

  13. Neuper C, Müller GR, Kübler A, Birbaumer N, Pfurtscheller G (2003) Clinical application of an EEG-based brain–computer interface: a case study in a patient with severe motor impairment. Clin Neurophysiol 114(3):399–409

    Article  Google Scholar 

  14. Obermaier B, Guger C, Neuper C, Pfurtscheller G (2001) Hidden markov models for online classification of single trial eeg data. Pattern Recognit Lett 22:1299–1309

    Article  MATH  Google Scholar 

  15. Obermaier B, Müller G, Pfurtscheller G (2003) ‘Virtual keyboard’ controlled by spontaneous EEG activity. IEEE Trans Neural Syst Rehabil Eng 11(4):422–426

    Article  Google Scholar 

  16. Pfurtscheller G, Neuper C (1997) Motor imagery activates primary sensorimotor area in humans. Neurosci Lett 239:65–68

    Article  Google Scholar 

  17. Pfurtscheller G, Neuper C (2001) Motor imagery and direct brain–computer communications. Proc IEEE 89:1123–1134

    Article  Google Scholar 

  18. Pfurtscheller G, Neuper C, Flotzinger D, Pregenzer M (1997) EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol 103:642–651

    Article  Google Scholar 

  19. Pfurtscheller G, Neuper C, Schlögl A, Luger K (1998) Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans Rehabil Eng 6(3):316–325

    Article  Google Scholar 

  20. Pfurtscheller G, Neuper C, Guger C, Harkam W, Ramoser H, Schlögl A, Obermaier B, Pregenzer M (2000) Current trends in graz brain–computer interface (BCI) research. IEEE Trans Rehabil Eng 8:216–219

    Article  Google Scholar 

  21. Pfurtscheller G, Neuper C, Müller G, Obermaier B, Krausz G, Schlögl A, Scherer R, Graimann B, Keinrath C, Skliris D, Woertz M, Supp G, Schrank C (2003) Graz-BCI: state of the art and clinical applications. IEEE Trans Neural Syst Rehabil Eng 11:177–180

    Article  Google Scholar 

  22. Scherer R, Müller-Putz G, Neuper C, Graimann B, Pfurtscheller G (2004) An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Trans Biomed Eng 51(6):979–984

    Article  Google Scholar 

  23. Schlögl A (2000) The electroencephalogram and the adaptive autoregressive model: theory and applications. Shaker Verlag, Aachen

    Google Scholar 

  24. Schlögl A (2005) The biosig project. 2003–2004. http://www.biosig.sf.net/

  25. Schlögl A, Flotzinger D, Pfurtscheller G (1997) Adaptive autoregressive modeling used for single-trial EEG classification. Biomed Tech 42:162–167

    Article  Google Scholar 

  26. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113:767–791

    Article  Google Scholar 

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Acknowledgments

We would like to thank our reviewers for their interest and useful comments. This work was supported by the Spanish Ministry of Culture and Education (Grant Ref.: AP-2000-4673), by the Lorenz Böhler Gesellschaft in Austria and by “Fonds zur Förderung der wissenschaftlichen Forschung” in Austria, project 16326-BO2.

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Authors and Affiliations

  1. Dp IEE, Edificio Los Tejos, Public University of Navarre, Campus Arrosadia s/n, Pamplona, 31006, Spain

    Carmen Vidaurre & Rafael Cabeza

  2. Institute for Knowledge Discovery, TUG, Krenngasse 37, Graz, 8010, Austria

    Reinhold Scherer & Gert Pfurtscheller

  3. Institute for Human–Computer Interfaces, TUG, Krenngasse 37, Graz, 8010, Austria

    Alois Schlögl

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  1. Carmen Vidaurre
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  2. Reinhold Scherer
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  3. Rafael Cabeza
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  4. Alois Schlögl
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  5. Gert Pfurtscheller
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Correspondence to Carmen Vidaurre.

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Vidaurre, C., Scherer, R., Cabeza, R. et al. Study of discriminant analysis applied to motor imagery bipolar data. Med Bio Eng Comput 45, 61–68 (2007). https://doi.org/10.1007/s11517-006-0122-5

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  • DOI: https://doi.org/10.1007/s11517-006-0122-5

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