PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Regularised CSP for Sensor Selection in BCI
Jason Farquhar, Jeremy Hill, Thomas Navin Lal and Bernhard Schölkopf
In: 3rd International Brain-Computer Interface Workshop and Training Course 2006, 21-24 Sept 2006, Graz, Austria.

Abstract

The Common Spatial Pattern (CSP) algorithm is a highly successful method for efficiently calculating spatial filters for brain signal classification. Spatial filtering can improve classification performance considerably, but demands that a large number of electrodes be mounted, which is inconvenient in day-to-day BCI usage. The CSP algorithm is also known for its tendency to overfit, i.e. to learn the noise in the training set rather than the signal. Both problems motivate an approach in which spatial filters are sparsified. We briefly sketch a reformulation of the problem which allows us to do this, using 1-norm regularisation. Focusing on the electrode selection issue, we present preliminary results on EEG data sets that suggest that effective spatial filters may be computed with as few as 10--20 electrodes, hence offering the potential to simplify the practical realisation of BCI systems significantly.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:2672
Deposited By:Jeremy Hill
Deposited On:22 November 2006