PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Optimizing Spatial Filters for BCI: Margin- and Evidence-Maximization Approaches
Jason Farquhar, Jeremy Hill and Bernhard Schölkopf
In: MAIA: Brain Computer Interfaces Workshop 2006, 8-9 Nov 2006, Rome, Italy.

Abstract

We present easy-to-use alternatives to the often-used two-stage Common Spatial Pattern~\cite{Koles1990CSP}+classifier approach for spatial filtering and classification of Event-Related Desychnronization signals in BCI. We report two algorithms that aim to optimize the spatial filters according to a criterion more directly related to the ability of the algorithms to generalize to unseen data. Both are based upon the idea of treating the spatial filter coefficients as hyperparameters for a kernel/covariance function defined over the normal CSP log-variance features. We then optimize these hyper-parameters directly along side the normal classifier parameters with respect to our chosen learning objective function. The two objectives considered are margin-maximization as used in Support-Vector Machines~\cite{Scholkopf2002LearnWKernels}, and the evidence maximization framework used in Gaussian Processes~\cite{Rasmussen2006GPforML}.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:2711
Deposited By:Jason Farquhar
Deposited On:22 November 2006