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Learning On-Line Classification via Decorrelated LMS Algorithm: Application to Brain-Computer Interfaces

  • Shiliang Sun
  • Changshui Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3735)

Abstract

The classification of time-varying neurophysiological signals, e.g., electroencephalogram (EEG) signals, advances the requirement of adaptability for classifiers. In this paper we address the challenge of neurophysiological signal classification arising from brain-computer interface (BCI) applications and propose an on-line classifier designed via the decorrelated least mean square (LMS) algorithm. Based on a Bayesian classifier with Gaussian mixture models, we derive the general formulation of gradient descent algorithms under the criterion of LMS. Further, to accelerate convergence, the decorrelated gradient instead of the instantaneous gradient is adopted for updating the parameters of the classifier adaptively. Utilizing the presented classifier for the off-line analysis of practical classification tasks in brain-computer interface applications shows its effectiveness and robustness compared to the stochastic gradient descent classifier which uses the instantaneous gradient directly.

Keywords

Gaussian Mixture Model Little Mean Square Gradient Descent Algorithm Stochastic Gradient Descent Little Mean Square Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Nicolelis, M.A.L.: Actions from Thoughts. Nature 409, 403–407 (2001)CrossRefGoogle Scholar
  2. 2.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-Computer Interfaces for Communication and Control. Clinical Neurophysiology 113, 767–791 (2002)CrossRefGoogle Scholar
  3. 3.
    Ebrahimi, T., Vesin, J.M., Garcia, G.: Brain-Computer Interfaces in Multimedia Communication. IEEE Signal Processing Magazine 20, 14–24 (2003)CrossRefGoogle Scholar
  4. 4.
    Wickelgren, I.: Tapping the Mind. Science 299, 496–499 (2003)CrossRefGoogle Scholar
  5. 5.
    Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G., Donchin, E., Quatrano, L.A., Robinson, C.J., Vaughan, T.M.: Brain-Computer Interface Technology: A Review of the First International Meeting. IEEE Transactions on Rehabilitation Engineering 8, 164–173 (2000)CrossRefGoogle Scholar
  6. 6.
    Vaughan, T.M.: Guest Editorial Brain-Computer Interface Technology: A Review of the Second International Meeting. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11, 94–109 (2003)CrossRefGoogle Scholar
  7. 7.
    Millán, J.R.: On the Need for On-Line Learning in Brain-Computer Interfaces. In: Proceedings of 2004 International Joint Conference on Neural Networks, Budapest, Hungary (2004)Google Scholar
  8. 8.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  9. 9.
    Saad, D.: On-Line Learning in Neural Networks. Cambridge University Press, Cambridge (1998)zbMATHGoogle Scholar
  10. 10.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, New York (2000)Google Scholar
  11. 11.
    Millán, J.R., Renkens, F., Mouriño, J., Gerstner, W.: Non-Invasive Brain-Actuated Control of a Mobile Robot. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pp. 1121–1126 (2003)Google Scholar
  12. 12.
    Millán, J.R., Renkens, F., Mouriño, J., Gerstner, W.: Brain-Actuated Interaction. Artificial Intelligence 159, 241–259 (2004)CrossRefGoogle Scholar
  13. 13.
    Millán, J.R., Renkens, F., Mouriño, J., Gerstner, W.: Noninvasive Brain-Actuated Control of a Mobile Robot by Human EEG. IEEE Transactions on Biomedical Engineering 51, 1026–1033 (2004)CrossRefGoogle Scholar
  14. 14.
    Mclachlan, G., Peel, D.: Finite Mixture Models. Wiley, New York (2000)CrossRefzbMATHGoogle Scholar
  15. 15.
    Glentis, G.O., Berberidis, K., Theodoridis, S.: Efficient Least Square Adaptive Algorithms for FIR Transversal Filtering. IEEE Signal Processing Magzine 16, 13–41 (1999)CrossRefGoogle Scholar
  16. 16.
    Doherty, J., Porayath, R.: A Robust Echo Canceler for Acoustic Environments. IEEE Transactions on Circuits and Systems II 44, 389–398 (1997)CrossRefGoogle Scholar
  17. 17.
    Perrin, R., Pernier, J., Bertrand, O., Echallier, J.: Spherical Spline for Potential and Current Density Mapping. Electroencephalography and Clinical Neurophysiology 72, 184–187 (1989)CrossRefGoogle Scholar
  18. 18.
    Perrin, R., Pernier, J., Bertrand, O., Echallier, J.: Corrigendum EEG 02274. Electroencephalography and Clinical Neurophysiology 76, 565 (1990)CrossRefGoogle Scholar
  19. 19.
    McFarland, D.J., McCane, L.M., David, S.V., Wolpaw, J.R.: Spatial Filter Selection for EEG-Based Communication. Electroencephalography and Clinical Neurophysiology 103, 386–394 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Shiliang Sun
    • 1
  • Changshui Zhang
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Department of AutomationTsinghua UniversityBeijingChina

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