Complexity synchronization analysis of neurophysiological data: Theory and methods

TL;DR

This paper introduces methods for analyzing neurophysiological data by assessing multifractal dimensions and complexity synchronization to understand information transfer in organ networks, aiming to improve validation and standardization.

Contribution

It advances the validation, standardization, and repeatability of MDEA and CS analysis methods for heterogeneous neurophysiological time series data.

Findings

Complexity of brain, heart, and lung signals co-vary during cognitive tasks.

Certain principles and guidelines are necessary for applying MDEA analysis.

Results demonstrate the potential of these methods to infer information transfer.

Abstract

We apply modified diffusion entropy analysis (MDEA) to assess multifractal dimensions of ON time series (ONTS) and complexity synchronization (CS) analysis to infer information transfer among ONs that are part of a network of organ networks (NoONs). The purpose of this paper is to advance the validation, standardization, and repeatability of MDEA and CS analysis of heterogeneous neurophysiological time series data. Results from processing these datasets show that the complexity of brain, heart, and lung ONTS significantly co-vary over time during cognitive task performance but that certain principles, guidelines, and strategies for the application of MDEA analysis need consideration.