Robust common spatial patterns based on Bhattacharyya distance and Gamma divergence

The computation of task-related spatial filters is a prerequisite for a successful application of motor imagery-based Brain-Computer Interfaces (BCI). However, in the presence of artifacts, e.g., resulting from eye movements or muscular activity, standard methods such as Common Spatial Patterns (CSP) perform poorly. Recently, a divergence-based spatial filter computation framework has been proposed which enables significantly more robust computation with respect to artifacts by using Beta divergence. In this paper we integrate two additional divergence measures, namely Bhattacharyya distance and Gamma divergence, into the divergence-based CSP framework and evaluate their robustness using simulations and data set IVa from BCI Competition III.