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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.
AbstractWe 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}. [Edit]
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