PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Identifying interactions in mixed and noisy complex systems
Guido Nolte, Andreas Ziehe, Frank Meinecke and Klaus-Robert Müller
Physical Review E Volume 73, Number 051913, 2006.

Abstract

We present a technique that identifies truly interacting subsystems of a complex system from multichannel data if the recordings are an unknown linear and instantaneous mixture of the true sources. The method is valid for arbitrary noise structure. For this, a blind source separation technique is proposed that diagonalizes antisymmetrized cross-correlation or cross-spectral matrices. The resulting decomposition finds truly interact- ing subsystems blindly and suppresses any spurious interaction stemming from the mixture. The usefulness of this interacting source analysis is demonstrated in simulations and for real electroencephalography data.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
Theory & Algorithms
ID Code:3288
Deposited By:Frank Meinecke
Deposited On:07 February 2008