A Minimax Approach to Optimize Spatial Filters for EEG Single-Trial Classification

EEG single-trial analysis requires methods that are robust with respect to noise, artifacts and nonstationarity among other problems. This work contributes by developing a minimax approach to robustify the common spatial patterns (CSP) algorithm. By optimizing the worst-case objective function within a prefixed set of the covariance matrices , we can transform the respective complex mathematical program into a simple generalized eigenvalue problem and thus obtain robust spatial filters very efficiently. We test our minimax CSP method with real world brain-computer interface (BCI) data sets in which we expect substantial fluctuations caused by day-to-day or paradigm-to-paradigm variability or different forms of stimuli. The results clearly show that the proposed method significantly improves the classical CSP approach in multiple BCI scenarios.