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Phys. Rev. E 80, 011138 (2009) [5 pages]

Inferring direct directed-information flow from multivariate nonlinear time series

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Michael Jachan1,2,3,*, Kathrin Henschel1,3, Jakob Nawrath1,2,4, Ariane Schad1, Jens Timmer1,3,4,5, and Björn Schelter1,3,4
1Center for Data Analysis and Modeling (FDM), University of Freiburg, Eckerstrasse 1, D-79104 Freiburg, Germany
2Department of Neurology, University Hospital of Freiburg, Breisacher Strasse 64, D-79098 Freiburg, Germany
3Bernstein Center for Computational Neuroscience (BCCN), University of Freiburg, Hansastrasse 9A, D-79104 Freiburg, Germany
4Department of Physics, University of Freiburg, Hermann Herder Strasse 3, D-79104 Freiburg, Germany
5Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Albertstrasse 19, D-79104 Freiburg, Germany

Received 12 February 2009; published 28 July 2009

Estimating the functional topology of a network from multivariate observations is an important task in nonlinear dynamics. We introduce the nonparametric partial directed coherence that allows disentanglement of direct and indirect connections and their directions. We illustrate the performance of the nonparametric partial directed coherence by means of a simulation with data from synchronized nonlinear oscillators and apply it to real-world data from a patient suffering from essential tremor.

© 2009 The American Physical Society

URL:
http://link.aps.org/doi/10.1103/PhysRevE.80.011138
DOI:
10.1103/PhysRevE.80.011138
PACS:
02.50.Ey, 02.50.Sk, 05.45.Tp

*michael.jachan@gmx.net; www.fdm.uni-freiburg.de/contact/team/jachan

 
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