title: Symbolic transfer entropy: inferring directionality in biosignals.
abstract: Inferring directional interactions from biosignals is of crucial importance to improve understanding of dynamical interdependences underlying various physiological and pathophysiological conditions. We here present symbolic transfer entropy as a robust measure to infer the direction of interactions between multidimensional dynamical systems. We demonstrate its performance in quantifying driver-responder relationships in a network of coupled nonlinear oscillators and in the human epileptic brain.
authors: Department of Epileptology, Neurophysics Group, University of Bonn, Sigmund-Freud-Str. 25, D-53105 Bonn, Germany. mattstan@gmx.de
authors: Matthäus
authors: M
authors: Staniek
authors: Klaus
authors: K
authors: Lehnertz
mesh_terms: D000818:Animals; D001921:Brain / Q000503:physiopathology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted