Abstract
We employed three different brain signal recording methods to perform Brain-Computer Interface studies on untrained subjects. In all cases, we aim to develop a system that could be used for fast, reliable preliminary screening in clinical BCI application, and we are interested in knowing how long screening sessions need to be. Good performance could be achieved, on average, after the first 200 trials in EEG, 75–100 trials in MEG, or 25–50 trials in ECoG. We compare the performance of Independent Component Analysis and the Common Spatial Pattern algorithm in each of the three sensor types, finding that spatial filtering does not help in MEG, helps a little in ECoG, and improves performance a great deal in EEG. In all cases the unsupervised ICA algorithm performed at least as well as the supervised CSP algorithm, which can suffer from poor generalization performance due to overfitting, particularly in ECoG and MEG.
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References
Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. Journal of Neuroscience Methods 134, 9–21 (2004)
Dornhege, G., Blankertz, B., Curio, G., Müller, K.R.: Increase information transfer rates in bci by csp extension to multi-class. NIPS 16 (2004)
Dornhege, G., Blankertz, B., Krauledat, M., Losch, F., Curio, G., Müller, K.R.: Optimizing spatio-temporal filters for improving Brain-Computer Interfacing. NIPS 18 (2006)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46, 389–422 (2002)
Koles, Z.J., Lazar, M.S., Zhou, S.Z.: Spatial patterns underlying population differences in the background EEG. Brain Topography 2(4), 275–284 (1990)
Kübler, A., Nijboer, F., Mellinger, J., Vaughan, T., Pawelzik, H., Schalk, G., McFarland, D., Birbaumer, N., Wolpaw, J.: Patients with ALS can use sensorimotor rhythms to operate a braincomputer interface. Neurology 64, 1775–1777 (2005)
Lal, T., Schröder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., Schölkopf, B.: Support vector channel selection in BCI. IEEE TBME 51(6), 1003–1010 (2004)
Lal, T., Schröder, M., Hill, N., Hinterberger, T., Mellinger, J., Rosenstiel, W., Hofmann, T., Birbaumer, N., Schölkopf, B.: A brain computer interface with online feedback based on magnetoencephalography. ICML 22, 465–472 (2005a)
Lal, T.N., Hinterberger, T., Widman, G., Schröder, M., Hill, N.J., Rosenstiel, W., Elger, C.E., Schölkopf, B., Birbaumer, N.: Methods towards invasive human brain computer interfaces. NIPS 17 (2005b)
Lemm, S., Blankertz, B., Curio, G., Müller, K.R.: Spatio-spectral filters for robust classification of single trial EEG. IEEE TBME 52(9), 993–1002 (2004)
Müller, K.R., Anderson, C.W., Birch, G.E.: Linear and nonlinear methods for brain-computer interfaces. IEEE TNSRE 11(2), 165–169 (2003)
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© 2006 Springer-Verlag Berlin Heidelberg
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Hill, N.J. et al. (2006). Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_41
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DOI: https://doi.org/10.1007/11861898_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44412-1
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