Extension of causal decomposition in the mutual complex dynamic process

TL;DR

This paper extends causal decomposition to mutual complex dynamic processes, enabling phase-based cause-effect analysis in physiological time series, with applications demonstrated in brain-muscle interactions.

Contribution

It introduces an extended causal decomposition method for mutual complex systems, applicable to physiological data, enhancing causality analysis beyond traditional approaches.

Findings

Effective in analyzing brain-muscle interactions

Applicable to complex physiological time series

Outperforms some existing methods in neuroscience

Abstract

Causal decomposition depicts a cause-effect relationship that is not based on the concept of prediction, but based on the phase dependence of time series. It has been validated in both stochastic and deterministic systems and is now anticipated for its application in the complex dynamic process. Here, we present an extension of causal decomposition in the mutual complex dynamic process: cause and effect of time series are inherited in the decomposition of intrinsic components in a similar time scale. Furthermore, we illustrate comparative studies with predominate methods used in neuroscience, and show the applicability of the method particularly to physiological time series in brain-muscle interactions, implying the potential to the causality analysis in the complex physiological process.