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  Classifying Event-Related Desynchronization in EEG, ECoG and MEG signals

Hill, N., Lal, T., Tangermann, M., Hinterberger, T., Widmann, G., Elger, C., et al. (2007). Classifying Event-Related Desynchronization in EEG, ECoG and MEG signals. In G. Dornhege, J. Millán, T. Hinterberger, J. McFarland, & K.-R. Müller (Eds.), Toward Brain-Computer Interfacing (pp. 235-260). Cambridge, MA, USA: MIT Press.

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 Creators:
Hill, NJ1, 2, Author              
Lal, TN1, 2, Author              
Tangermann, M, Author
Hinterberger , T, Author
Widmann, G, Author
Elger, CE, Author
Schölkopf, B1, 2, Author              
Birbaumer, N, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: We present the results from three motor imagery-based brain-computer interface experiments. Brain signals were recorded from eight untrained subjects using EEG, four using ECoG, and ten using MEG. In all cases, we aim to develop a system that could be used for fast, reliable preliminary screening in the clinical application of a BCI, so we aim to obtain the best possible classification performance in a short time. Accordingly, the burden of adaptation is on the side of the computer rather than the user, so we must adopt a machine learning approach to the analysis. We introduce the required machine-learning vocabulary and concepts, and then present quantitative results that focus on two main issues. The first is the effect of the number of trials—how long does the recording session need to be? We find that good performance could be achieved, on average, after the first 200 trials in EEG, 75–100 trials in MEG, and 25–50 trials in ECoG. The second issue is the effect of spatial filtering—we compare the performance of the original sensor signals with that of the outputs of independent component Analysis and the common spatial pattern algorithm, in each of the three sensor types. We find that spatial filtering does not help in MEG, helps a little in ECoG, and improves performance a great deal in EEG. The unsupervised ICA algorithm performed at least as well as the supervised CSP algorithm in all cases—the latter suffered from poor generalization performance due to overfitting in ECoG and MEG, although this could be alleviated by reducing the number of sensors used as input to the algorithm.

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 Dates: 2007-09
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 4035
 Degree: -

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Title: Toward Brain-Computer Interfacing
Source Genre: Book
 Creator(s):
Dornhege, G, Editor
Millán , JR, Editor
Hinterberger, T, Editor
McFarland, J, Editor
Müller, K-R1, Editor            
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: 507 Volume / Issue: - Sequence Number: - Start / End Page: 235 - 260 Identifier: ISBN: 0-262-04244-4