title: Detection of directed information flow in biosignals.
abstract: Several analysis techniques have been developed for time series to detect interactions in multidimensional dynamic systems. When analyzing biosignals generated by unknown dynamic systems, awareness of the different concepts upon which these analysis techniques are based, as well as the particular aspects the methods focus on, is a basic requirement for drawing reliable conclusions. For this purpose, we compare four different techniques for linear time series analysis. In general, these techniques detect the presence of interactions, as well as the directions of information flow, in a multidimensional system. We review the different conceptual properties of partial coherence, a Granger causality index, directed transfer function, and partial directed coherence. The performance of these tools is demonstrated by application to linear dynamic systems.
authors.affiliation: Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany. matthias.winetrhalder@fdm.uni-freiburg.de
authors.forename: Matthias
authors.initials: M
authors.lastname: Winterhalder
authors.forename: Björn
authors.initials: B
authors.lastname: Schelter
authors.forename: Wolfram
authors.initials: W
authors.lastname: Hesse
authors.forename: Karin
authors.initials: K
authors.lastname: Schwab
authors.forename: Lutz
authors.initials: L
authors.lastname: Leistritz
authors.forename: Jens
authors.initials: J
authors.lastname: Timmer
authors.forename: Herbert
authors.initials: H
authors.lastname: Witte
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted