title: Inferring direct directed-information flow from multivariate nonlinear time series.
abstract: 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.
authors: Center for Data Analysis and Modeling (FDM), University of Freiburg, and Department of Neurology, University Hospital of Freiburg, Breisacher Strasse 64, D-79098 Freiburg, Germany. michael.jachan@gmx.net
authors: Michael
authors: M
authors: Jachan
authors: Kathrin
authors: K
authors: Henschel
authors: Jakob
authors: J
authors: Nawrath
authors: Ariane
authors: A
authors: Schad
authors: Jens
authors: J
authors: Timmer
authors: Björn
authors: B
authors: Schelter