Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Overview

Journal Brain Sci

Publisher MDPI

Date 2023 May 27

PMID 37239285

Authors

Shaida Kargarnovin

Christopher Hernandez

Farzad V Farahani

Waldemar Karwowski

Affiliations

Soon will be listed here.

Abstract

(1) Background: Chaos, a feature of nonlinear dynamical systems, is well suited for exploring biological time series, such as heart rates, respiratory records, and particularly electroencephalograms. The primary purpose of this article is to review recent studies using chaos theory and nonlinear dynamical methods to analyze human performance in different brain processes. (2) Methods: Several studies have examined chaos theory and related analytical tools for describing brain dynamics. The present study provides an in-depth analysis of the computational methods that have been proposed to uncover brain dynamics. (3) Results: The evidence from 55 articles suggests that cognitive function is more frequently assessed than other brain functions in studies using chaos theory. The most frequently used techniques for analyzing chaos include the correlation dimension and fractal analysis. Approximate, Kolmogorov and sample entropy account for the largest proportion of entropy algorithms in the reviewed studies. (4) Conclusions: This review provides insights into the notion of the brain as a chaotic system and the successful use of nonlinear methods in neuroscience studies. Additional studies of brain dynamics would aid in improving our understanding of human cognitive performance.

Citing Articles

Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis.

Hernandez C, Kargarnovin S, Hejazi S, Karwowski W Front Comput Neurosci. 2023; 17:1207067.

PMID: 37457899 PMC: 10344458. DOI: 10.3389/fncom.2023.1207067.

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