Identifying interactions in mixed and noisy complex systems.

Guido Nolte; Frank C Meinecke; Andreas Ziehe; Klaus-Robert Müller

doi:10.1103/physreve.73.051913

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Identifying interactions in mixed and noisy complex systems.

Guido Nolte·Frank C Meinecke·Andreas Ziehe·Klaus-Robert Müller

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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.

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Action Potentials Algorithms Animals Brain Computer Simulation Diagnosis, Computer-Assisted Electroencephalography Humans Models, Neurological Nerve Net Synaptic Transmission Evaluation Study Journal Article Research Support, Non-U.S. Gov't

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Guido Nolte, Frank C Meinecke, Andreas Ziehe, Klaus-Robert Müller. 2006-05-23. Identifying interactions in mixed and noisy complex systems.. https://doi.org/10.1103/physreve.73.051913

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