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Erschienen in:
2009 | OriginalPaper | Buchkapitel
A Maxmin Approach to Optimize Spatial Filters for EEG Single-Trial Classification
verfasst von : Motoaki Kawanabe, Carmen Vidaurre, Benjamin Blankertz, Klaus-Robert Müller
Erschienen in: Bio-Inspired Systems: Computational and Ambient Intelligence
Verlag: Springer Berlin Heidelberg
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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.