Bivariate phase-rectified signal averaging

TL;DR

This paper introduces a bivariate phase-rectified signal averaging method to analyze relationships between two non-stationary signals, outperforming traditional cross-correlation in noisy and nonlinear contexts.

Contribution

The paper presents a novel bivariate PRSA technique that effectively studies inter-relationships and coupling directions between two simultaneous signals, especially in non-stationary and nonlinear conditions.

Findings

Bivariate PRSA can analyze events in one signal based on the phase of another.

It is robust against noise and non-stationarities.

It outperforms traditional cross-correlation analysis in complex scenarios.

Abstract

Phase-Rectified Signal Averaging (PRSA) was shown to be a powerful tool for the study of quasi-periodic oscillations and nonlinear effects in non-stationary signals. Here we present a bivariate PRSA technique for the study of the inter-relationship between two simultaneous data recordings. Its performance is compared with traditional cross-correlation analysis, which, however, does not work well for non-stationary data and cannot distinguish the coupling directions in complex nonlinear situations. We show that bivariate PRSA allows the analysis of events in one signal at times where the other signal is in a certain phase or state; it is stable in the presence of noise and impassible to non-stationarities.