Abstract
In this paper, we present a method of feature extraction for motor imagery single trial EEG classification, where we exploit nonnegative matrix factorization (NMF) to select discriminative features in the time-frequency representation of EEG. Experimental results with motor imagery EEG data in BCI competition 2003, show that the method indeed finds meaningful EEG features automatically, while some existing methods should undergo cross-validation to find them.
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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)
Ebrahimi, T., Vesin, J.F., Garcia, G.: Brain-computer interface in multimedia communication. IEEE Signal Processing Magazine 20, 14–24 (2003)
Lal, T.N., Schroder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., Schölkopf, B.: Support vector channel selection in BCI. Technical Report 120, Max Planck Institute for Biological Cybernetics (2003)
Cichocki, A., Zdunek, R., Amari, S.: Csiszár’s divergences for non-negative matrix factorization: Family of new algorithms. In: Proc. Int’l Conf. Independent Component Analysis and Blind Signal Separation, Charleston, South Carolina (2006)
Cichocki, A., Zdunek, R., Amari, S.: New algorithms for non-negative matrix factorization in applications to blind source separation. In: Proc. IEEE Int’l Conf. Acoustics, Speech, and Signal Processing, Toulouse, France (2006)
Cichocki, A., Choi, S.: Nonnegative matrix factorization with α-divergence. Pattern Recognition Letters (2006) (submitted)
Lemm, S., Schäfer, C., Curio, G.: BCI competition 2003-data set III: Probabilistic modeling of sensorimotor μ rhythms for classification of imaginary hand movements. IEEE Trans. Biomedical Engineering 51 (2004)
Paatero, P., Tapper, U.: Least squares formulation of robust non-negative factor analysis. Chemometrics Intelligent Laboratory Systems 37, 23–35 (1997)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, vol. 13. MIT Press, Cambridge (2001)
Blankertz, B., Müller, K.R., Curio, G., Vaughan, T.M., Schalk, G., Wolpaw, J.R., Schlögl, A., Neuper, C., Pfurtscheller, G., Hinterberger, T., Schroder, M., Birbaumer, N.: The BCI competition 2003: Progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans. Biomedical Engineering 51 (2004)
Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research 5, 1457–1469 (2004)
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© 2006 Springer-Verlag Berlin Heidelberg
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Lee, H., Cichocki, A., Choi, S. (2006). Nonnegative Matrix Factorization for Motor Imagery EEG Classification. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_26
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DOI: https://doi.org/10.1007/11840930_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
Online ISBN: 978-3-540-38873-9
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