关联维数
混乱的
非线性系统
计算机科学
混沌理论
动力系统理论
样本熵
人工智能
认知
熵(时间箭头)
分形维数
分形
递归量化分析
统计物理学
心理学
神经科学
数学
模式识别(心理学)
物理
数学分析
量子力学
作者
Shaida Kargarnovin,Christopher Hernandez,Farzad V. Farahani,Waldemar Karwowski
出处
期刊:Brain Sciences
[MDPI AG]
日期:2023-05-17
卷期号:13 (5): 813-813
被引量:2
标识
DOI:10.3390/brainsci13050813
摘要
(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.
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