Adapting Spatial Filter Methods for Nonstationary BCIs

Tomioka, R; Hill, JN; Blankertz, B; Aihara, K

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Adapting Spatial Filter Methods for Nonstationary BCIs

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Hill, JN
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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Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CF8D-6

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]).