title: Identifying interactions in mixed and noisy complex systems.
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.
authors.affiliation: Fraunhofer FIRST.IDA, Kekuléstrasse 7, D-12489 Berlin, Germany. nolte@first.fhg.de
authors.forename: Guido
authors.initials: G
authors.lastname: Nolte
authors.forename: Frank C
authors.initials: FC
authors.lastname: Meinecke
authors.forename: Andreas
authors.initials: A
authors.lastname: Ziehe
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*