Spectral Time-Varying Pattern Causality and Its Application.

Abstract

In this paper, a new method based on delayed pattern causality is proposed, called spectral time-varying pattern causality. Specifically, this method uses symbolic dynamics and phase space reconstruction to infer causality, systematically quantifies the causal relationship between different frequency components of the signal, and the generated spectrum provides a rich information representation of the time-varying potential causality. The causal intensity at different times is quantified by a sliding window, providing a dynamic perspective for the study of causality in complex systems. Through the simulation data, we verified the effectiveness of the method and its robustness to noise, and then applied it to physiological data to compare the differences in coupling between electrodes in different brain regions between normal and Parkinson's patients in the resting state. The study of causality in complex systems provides a new perspective to better capture the latent and elusive dynamic structures.