title: Inferring directional interactions from transient signals with symbolic transfer entropy.
abstract: We extend the concept of symbolic transfer entropy to enable the time-resolved investigation of directional relationships between coupled dynamical systems from short and transient noisy time series. For our approach, we consider an observed ensemble of a sufficiently large number of time series as multiple realizations of a process. We derive an index that quantifies the preferred direction of transient interactions and assess its significance using a surrogate-based testing scheme. Analyzing time series from noisy chaotic systems, we demonstrate numerically the applicability and limitations of our approach. Our findings obtained from an analysis of event-related brain activities underline the importance of our method to improve understanding of gross neural interactions underlying cognitive processes.
authors.affiliation: Department of Epileptology, University of Bonn, Bonn, Germany. marcel.martini@gmx.de
authors.forename: Marcel
authors.initials: M
authors.lastname: Martini
authors.forename: Thorsten A
authors.initials: TA
authors.lastname: Kranz
authors.forename: Tobias
authors.initials: T
authors.lastname: Wagner
authors.forename: Klaus
authors.initials: K
authors.lastname: Lehnertz
mesh_terms: D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology; D019277:Entropy; D005071:Evoked Potentials; D008959:Models, Neurological