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Identifying interactions in mixed and noisy complex systems

Guido Nolte* and Frank C. Meinecke†

Andreas Ziehe‡

Klaus-Robert Müller§

  • Fraunhofer FIRST.IDA, Kekuléstrasse 7, D-12489 Berlin, Germany

  • Fraunhofer FIRST.IDA, Kekuléstrasse 7, D-12489 Berlin, Germany and Technical University Berlin, Institute for Software Engineering, Franklinstrasse 28/29, 10587 Berlin, Germany

  • Fraunhofer FIRST.IDA, Kekuléstrasse 7, D-12489 Berlin, Germany and Institut für Informatik, Universität Potsdam, August-Bebel Strasse 89, D-14482 Potsdam, Germany

  • *Electronic address: nolte@first.fhg.de
  • †Electronic address: meinecke@first.fhg.de
  • ‡Electronic address: ziehe@first.fhg.de
  • §Electronic address: klaus@first.fhg.de
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Phys. Rev. E 73, 051913 – Published 23 May, 2006

DOI: https://doi.org/10.1103/PhysRevE.73.051913

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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Outline

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Phys. Rev. E 73, 051913– Published 23 May, 2006
  • Received 5 August 2005

© 2006 American Physical Society

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