ROBUST COMMON SPATIAL PATTERNS BY MINIMUM DIVERGENCE COVARIANCE ESTIMATOR

Reliable estimation of covariance matrices from high-dimen-sional electroencephalographic recordings is crucial for a suc-cessful application of Brain-Computer Interface (BCI) sys-tems. Artifactual trials and non-stationarity effects may have a large impact on the estimation quality and adversely affect the spatial filter computation and consequently the classifi-cation accuracy of the system. In this work we propose a novel robust estimator for covariance matrices that takes into account the trial structure of BCI experiments. Our estimator minimizes beta divergence between the empirical and a model Wishart distribution, thus allows to robustly average the esti-mated covariance matrices of different trials and downweight the influence of outlier trials. We evaluate this novel estimator on a data set with recordings from 80 subjects