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Adapting Spatial Filter Methods for Nonstationary BCIs

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84261

Tomioka,  R
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83968

Hill,  JN
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

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


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-CF8D-6
Zusammenfassung
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]).