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  Adapting Spatial Filter Methods for Nonstationary BCIs

Tomioka, R., Hill, J., Blankertz, B., & Aihara, K. (2006). Adapting Spatial Filter Methods for Nonstationary BCIs. 2006 Workshop on Information-Based Induction Sciences (IBIS 2006), 65-70.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CF8D-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-968F-2
Genre: Conference Paper

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IBIS-2006-Workshop-Hill.pdf (Any fulltext), 601KB
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 Creators:
Tomioka, R, Author              
Hill, JN1, 2, Author              
Blankertz, B, Author
Aihara, K, 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, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the possible nonstationarity in the data from the datablock the method is trained on and that the method is applied to. Assuming the joint distributions of the whitened signal and the class label to be identical in two blocks, where the whitening is done in each block independently, we propose a simple adaptation formula that is applicable to a broad class of spatial filtering methods including ICA, CSP, and logistic regression classifiers. We characterize the class of linear transformations for which the above assumption holds. Experimental results on 60 BCI datasets show improved classification accuracy compared to (a) fixed spatial filter approach (no adaptation) and (b) fixed spatial pattern approach (proposed by Hill et al., 2006 [1]).

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 Dates: 2006-11
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 4247
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Title: 2006 Workshop on Information-Based Induction Sciences (IBIS 2006)
Place of Event: Osaka, Japan
Start-/End Date: 2006-10-31 - 2006-11-02

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Title: 2006 Workshop on Information-Based Induction Sciences (IBIS 2006)
Source Genre: Journal
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 65 - 70 Identifier: -