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

Abstract

Electroencephalographic 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 eigenvalue 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.KeywordsLinear Discriminant AnalysisSpatial FilterGeneralize Eigenvalue ProblemRayleigh QuotientCommon Spatial PatternThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.