Staniek Matthäus, Lehnertz Klaus
Department of Epileptology, Neurophysics Group, University of Bonn, Sigmund-Freud-Str. 25, D-53105 Bonn, Germany.
Biomed Tech (Berl). 2009 Dec;54(6):323-8. doi: 10.1515/BMT.2009.040.
Inferring directional interactions from biosignals is of crucial importance to improve understanding of dynamical interdependences underlying various physiological and pathophysiological conditions. We here present symbolic transfer entropy as a robust measure to infer the direction of interactions between multidimensional dynamical systems. We demonstrate its performance in quantifying driver-responder relationships in a network of coupled nonlinear oscillators and in the human epileptic brain.
从生物信号中推断方向性相互作用对于增进对各种生理和病理生理状况背后动态相互依存关系的理解至关重要。我们在此提出符号转移熵，作为一种推断多维动态系统之间相互作用方向的稳健度量。我们展示了它在量化耦合非线性振荡器网络和人类癫痫大脑中的驱动-响应关系方面的性能。
