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

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.

Keywords: EEG; chaos theory; neuroscience; nonlinear dynamical systems; performance.

Figure 1

Flow diagram of the methods and selection processes used in this review, according to the PRISMA guidelines [14].

Figure 2

Representation of studies before and after 2011.

Figure 3

Categorization of the included studies.

Figure 4

Frequency of methods utilized to analyze chaotic signals.

Figure 5

Frequency of the entropy measures among the included studies.

Figure 6

Network of keywords co-occurrence. (*) A consolidated representation of different variations of that keyword.