title: Causality networks from multivariate time series and application to epilepsy.
abstract: Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. For this, realizations on high dimensional coupled dynamical systems are considered and the performance of the Granger causality measures is evaluated, seeking for the measures that form networks closest to the true network of the dynamical system. In particular, the comparison focuses on Granger causality measures that reduce the state space dimension when many variables are observed. Further, the linear and nonlinear Granger causality measures of dimension reduction are compared to a standard Granger causality measure on electroencephalographic (EEG) recordings containing episodes of epileptiform discharges.
authors.forename: Elsa
authors.initials: E
authors.lastname: Siggiridou
authors.forename: Christos
authors.initials: C
authors.lastname: Koutlis
authors.forename: Alkiviadis
authors.initials: A
authors.lastname: Tsimpiris
authors.forename: Vasilios K
authors.initials: VK
authors.lastname: Kimiskidis
authors.forename: Dimitris
authors.initials: D
authors.lastname: Kugiumtzis
mesh_terms: D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics