Combined classification and channel/basis selection with L1-L2 regularization with application to P300 speller system
Ryota Tomioka and Stefan Haufe
In: 4th International Brain-Computer Interface Workshop and Training Course, 18-21 Sep 2008, Graz, Austria.

Abstract

We propose a method that combines single-trial classication and channel/basis selection in a single regularized empirical risk minimization problem. We use the linear sum of the Euclidian norms of the columns of the coecient matrix as the regularizer. This penalty enables us to select rows and columns of the coecient matrix, which correspond to a subset of the channels or a subset of basis functions, in a systematic manner. Moreover, the parameter learning can be performed in a convex optimization problem with second order cone constraints. The method is demonstrated on P300 speller dataset (dataset II) from the BCI competition III. The method performs reasonably well with small number of electrodes/basis functions.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.

[Edit]