PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A maxmin approach to optimize spatial filters for EEG single-trial classification
Motoaki Kawanabe, Carmen Vidaurre, Benjamin Blankertz and Klaus-Robert Müller
Proceedings of IWANN 09, Part I, LNCS pp. 674-682, 2009.

Abstract

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:6461
Deposited By:Stefan Haufe
Deposited On:08 March 2010