PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Identifying interactions in mixed and noisy complex systems
Guido Nolte, Frank Meinecke, Andreas Ziehe 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 interacting 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
Theory & Algorithms
ID Code:2441
Deposited By:Andreas Ziehe
Deposited On:22 November 2006