Estimating nonlinear interdependences in dynamical systems using cellular nonlinear networks.
We propose a method for estimating nonlinear interdependences between time series using cellular nonlinear networks. Our approach is based on the nonlinear dynamics of interacting nonlinear elements. We apply it to time series of coupled nonlinear model systems and to electroencephalographic time series from an epilepsy patient, and we show that an accurate approximation of symmetric and asymmetric realizations of a nonlinear interdependence measure can be achieved, thus allowing one to detect the strength and direction of couplings.
Krug
Dieter
D
Department of Epileptology, University of Bonn, Sigmund-Freud-Strasse 25, 53105 Bonn, Germany. krug@uni-bonn.de
Osterhage
Hannes
H
Elger
Christian E
CE
Lehnertz
Klaus
K
D000200:Action Potentials; D000818:Animals; D002450:Cell Communication; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D012372:ROC Curve; D012640:Seizures / Q000175:diagnosis; D013997:Time Factors