Kawanabe, M.M.KawanabeVidaurre, C.C.VidaurreBlankertz, B.B.BlankertzMüller, K.-R.K.-R.Müller2022-03-112022-03-112009https://publica.fraunhofer.de/handle/publica/36322510.1007/978-3-642-02478-8_842-s2.0-68749091411Electroencephalographic single-trial analysis requires methods that are robust with respect to noise, artifacts and non-stationarity among other problems. This work contributes by developing a maxmin 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 eigen-value problem and thus obtain robust spatial filters very efficiently. We test our maxmin 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.en004400conference paper