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

MPG-Autoren
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Hill,  NJ
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Lal,  TN
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

https://link.springer.com/content/pdf/10.1007%2F11861898_41.pdf
(Verlagsversion)

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Zitation

Hill, N., Lal, T., Schröder, M., Hinterberger, T., Widman, G., Elger, C., et al. (2006). Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals. In K. Franke, K.-R. Müller, B. Nickolay, & R. Scäfer (Eds.), Pattern Recognition: 28th DAGM Symposium, Berlin, Germany, September 12-14, 2006 (pp. 404-413). Berlin, Germany: Springer.


Zitierlink: https://hdl.handle.net/21.11116/0000-0004-99B0-8
Zusammenfassung
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.