Symbolic transfer entropy: inferring directionality in biosignals.
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.
Staniek
Matthäus
M
Department of Epileptology, Neurophysics Group, University of Bonn, Sigmund-Freud-Str. 25, D-53105 Bonn, Germany. mattstan@gmx.de
Lehnertz
Klaus
K
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