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Identifying delayed directional couplings with symbolic transfer entropy.
Henning Dickten1, Klaus Lehnertz1
1Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany and Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany and Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|January 24, 2015
Summary
We developed a new method to find delayed directional relationships in complex systems using symbolic transfer entropy. This approach helps build better functional network structures from time series data, including in the human epileptic brain.
Area of Science:
- Dynamical systems theory
- Information theory
- Neuroscience
Background:
- Investigating directional relationships in coupled dynamical systems is crucial for understanding complex interactions.
- Existing methods may not fully capture delayed interactions.
- Time series analysis is fundamental in many scientific fields.
Purpose of the Study:
- To extend symbolic transfer entropy for analyzing delayed directional relationships in coupled dynamical systems.
- To assess the applicability and limitations of the proposed method.
- To improve the construction of functional network structures from time series data.
Main Methods:
- A straightforward extension of symbolic transfer entropy was proposed.
- The method was tested on time series data from chaotic model systems.
- The approach was applied to infer delayed directed interactions in the human epileptic brain.
Main Results:
- The study demonstrated the applicability of the extended symbolic transfer entropy.
- Limitations of the approach were identified through analysis of model systems.
- The method successfully inferred delayed directed interactions in epileptic brain data.
Conclusions:
- The proposed extension of symbolic transfer entropy is effective for investigating delayed directional relationships.
- The findings highlight the method's utility in constructing functional networks from data.
- This approach offers valuable insights for neuroscience and complex systems analysis.
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