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Time-Dependent Demixing of Task-Relevant EEG 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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Farquhar,  J
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

http://www.neurotechcenter.org/sites/default/files/misc/Time-dependent%20demixing%20of%20task-relevant%20EEG%20signals.pdf
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Zitation

Hill, N., Farquhar, J., Lal, T., & Schölkopf, B. (2006). Time-Dependent Demixing of Task-Relevant EEG Signals. In G. Müller-Putz (Ed.), 3rd International Brain-Computer Interface Workshop and Training Course 2006 (pp. 20-21). Graz, Austria: Verlag der Technischen Universität Graz.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-D069-D
Zusammenfassung
Given a spatial filtering algorithm that has allowed us to identify task-relevant EEG sources, we present a simple approach
for monitoring the activity of these sources while remaining relatively robust to changes in other (task-irrelevant) brain activity. The idea is to keep spatial *patterns* fixed rather than spatial filters, when transferring from
training to test sessions or from one time window to another. We show that a fixed spatial pattern (FSP)
approach, using a moving-window estimate of signal covariances, can be more robust to non-stationarity than a fixed spatial filter (FSF) approach.