A Robust Estimation of Information Flow in Coupled Nonlinear Systems

  • Shivkumar Sabesan
  • Konstantinos Tsakalis
  • Andreas Spanias
  • Leon Iasemidis
Part of the Springer Optimization and Its Applications book series (SOIA, volume 38)


Transfer entropy (TE) is a recently proposed measure of the information flow between coupled linear or nonlinear systems. In this study, we first suggest improvements in the selection of parameters for the estimation of TE that significantly enhance its accuracy and robustness in identifying the direction and the level of information flow between observed data series generated by coupled complex systems. Second, a new measure, the net transfer of entropy (NTE), is defined based on TE. Third, we employ surrogate analysis to show the statistical significance of the measures. Fourth, the effect of measurement noise on the measures’ performance is investigated up to \(S/N = 3\) dB. We demonstrate the usefulness of the improved method by analyzing data series from coupled nonlinear chaotic oscillators. Our findings suggest that TE and NTE may play a critical role in elucidating the functional connectivity of complex networks of nonlinear systems.


Autocorrelation Function Information Flow Surrogate Data Transfer Entropy Drive Oscillator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by NSF (Grant ECS-0601740) and the Science Foundation of Arizona (Competitive Advantage Award CAA 0281-08).


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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.The Harrington Department of BioengineeringArizona State UniversityTempeUSA
  2. 2.Barrow Neurological InstitutePhoenixUSA
  3. 3.Department of Electrical EngineeringArizona State UniversityTempeUSA
  4. 4.Mayo ClinicPhoenixUSA

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