Symbolic transfer entropy.
We propose to estimate transfer entropy using a technique of symbolization. We demonstrate numerically that symbolic transfer entropy is a robust and computationally fast method to quantify the dominating direction of information flow between time series from structurally identical and nonidentical coupled systems. Analyzing multiday, multichannel electroencephalographic recordings from 15 epilepsy patients our approach allowed us to reliably identify the hemisphere containing the epileptic focus without observing actual seizure activity.
Department of Epileptology, Neurophysics Group, University of Bonn, Sigmund-Freud-Strasse 25, Bonn, Germany. mattstan@gmx.de
Matthäus
M
Staniek
Klaus
K
Lehnertz
D001921:Brain / Q000503:physiopathology / Q000601:surgery; D004566:Electrodes; D004569:Electroencephalography; D019277:Entropy; D004833:Epilepsy, Temporal Lobe / Q000503:physiopathology* / Q000601:surgery*; D006801:Humans; D008959:Models, Neurological; D008962:Models, Theoretical