Identifying delayed directional couplings with symbolic transfer entropy

TL;DR

This paper introduces an extension of symbolic transfer entropy to detect delayed directional interactions in coupled dynamical systems, with applications to brain data, enhancing functional network analysis.

Contribution

The paper presents a novel extension of symbolic transfer entropy specifically designed to identify delayed directional couplings in time series data.

Findings

Successfully applied to chaotic model systems

Effective in inferring delayed interactions in epileptic brain data

Highlights importance for functional network construction

Abstract

We propose a straightforward extension of symbolic transfer entropy to enable the investigation of delayed directional relationships between coupled dynamical systems from time series. Analyzing time series from chaotic model systems, we demonstrate the applicability and limitations of our approach. Our findings obtained from applying our method to infer delayed directed interactions in the human epileptic brain underline the importance of our approach for improving the construction of functional network structures from data.