The problem of reconstructing nonlinear and complex dynamical systems from measured
data or time series is central to many scientific disciplines including physical, biological …

Network theory provides novel concepts that promise an improved characterization of
interacting dynamical systems. Within this framework, evolving networks can be considered …

This technical paper offers a critical re-evaluation of (spectral) Granger causality measures
in the analysis of biological timeseries. Using realistic (neural mass) models of coupled …

Quantifying the functional relations between the nodes in a network based on local
observations is a key challenge in studying complex systems. Most existing time series …

This tutorial paper introduces a common framework for the evaluation of widely used
frequency‐domain measures of coupling (coherence, partial coherence) and causality …

Granger-causality metrics have become increasingly popular tools to identify directed
interactions between brain areas. However, it is known that additive noise can strongly affect …

Inferring causal interactions from observed data is a challenging problem, especially in the
presence of measurement noise. To alleviate the problem of spurious causality, Haufe …

Interest in brain connectivity inference has become ubiquitous and is now increasingly
adopted in experimental investigations of clinical, behavioral, and experimental …

We studied causal relations among heart period (HP), systolic arterial pressure (SAP) and
respiration (R) according to the definition of Granger causality in the time domain. Autonomic …

Learning the causal graph in a complex system is crucial for knowledge discovery and
decision making, yet it remains a challenging problem because of the unknown nonlinear …