PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Finding stationary subspaces in multivariate time series
Paul von Buenau, Frank C. Meinecke, Franz Kiraly and Klaus-Robert Mueller
Physical Review Letters Volume 103, Number 21, 2009. ISSN 1079-7114

Abstract

Identifying temporally invariant components in complex multivariate time series is key to under- standing the underlying dynamical system and predict its future behavior. In this Letter, we propose a novel technique, stationary subspace analysis (SSA), that decomposes a multivariate time series into its stationary and nonstationary part. The method is based on two assumptions: (a) the observed signals are linear superpositions of stationary and nonstationary sources; and (b) the nonstationarity is measurable in the first two moments. We characterize theoretical and practical properties of SSA and study it in simulations and cortical signals measured by electroencephalography. Here, SSA succeeds in finding stationary components that lead to a significantly improved prediction accuracy and meaningful topo- graphic maps which contribute to a better understanding of the underlying nonstationary brain processes.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Brain Computer Interfaces
Theory & Algorithms
ID Code:6919
Deposited By:Paul Buenau
Deposited On:15 April 2010