PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Robust common spatial filters with a maxmin approach
Motoaki Kawanabe, Carmen Vidaurre, Simon Scholler, Benjamin Blankertz and Klaus-Robert Müller
EMBS-Conference pp. 2470-2473, 2009.

Abstract

Electroencephalographic signals are known to be non-stationary and easily affected by artifacts, therefore their analysis requires methods that can deal with noise. In this work we present two ways of calculating robust common spatial patterns under a maxmin approach. The worst-case objective function is optimized within prefixed sets of the covariance matrices that are defined either very simply as identity matrices or in a data driven way using PCA. We test common spatial filters derived with these two approaches with real world brain-computer interface (BCI) data sets in which we expect substantial “day-to-day” fluctuations (session transfer problem). We compare our results with the classical common spatial filters and show that both can improve the performance of the latter.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:6465
Deposited By:Stefan Haufe
Deposited On:08 March 2010