Robust common spatial patterns by minimum divergence covariance estimator

Reliable estimation of covariance matrices from high-dimensional electroencephalographic recordings is crucial for a successful application of Brain-Computer Interface (BCI) systems. 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 classification 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 estimated 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.