Version 1
: Received: 7 October 2024 / Approved: 8 October 2024 / Online: 9 October 2024 (12:26:12 CEST)
How to cite:
Odan, S. Complexity Measures in Diverse Dynamical Regimes and Application to States of Consciousness. Preprints2024, 2024100649. https://doi.org/10.20944/preprints202410.0649.v1
Odan, S. Complexity Measures in Diverse Dynamical Regimes and Application to States of Consciousness. Preprints 2024, 2024100649. https://doi.org/10.20944/preprints202410.0649.v1
Odan, S. Complexity Measures in Diverse Dynamical Regimes and Application to States of Consciousness. Preprints2024, 2024100649. https://doi.org/10.20944/preprints202410.0649.v1
APA Style
Odan, S. (2024). Complexity Measures in Diverse Dynamical Regimes and Application to States of Consciousness. Preprints. https://doi.org/10.20944/preprints202410.0649.v1
Chicago/Turabian Style
Odan, S. 2024 "Complexity Measures in Diverse Dynamical Regimes and Application to States of Consciousness" Preprints. https://doi.org/10.20944/preprints202410.0649.v1
Abstract
The human brain, a highly complex dynamical system, exhibits various states of consciousness—such as wakefulness, sleep, and altered states—each characterized by distinct patterns of neural activity. To capture the dynamical properties of these states, a range of complexity measures is utilized, with a primary focus on Statistical Complexity (SC) and Lempel-Ziv complexity (LZc), and supplemented by Approximate Entropy (ApEn) and Kolmogorov Complexity (KC). These measures are applied to both simulated data, generated through logistic maps and Multivariate Autoregressive (MVAR) models, and intracranial depth electrode recordings from patients. The results demonstrate that these complexity measures effectively capture intricate dynamics of the brain across different states. Specifically, SC captures the structural complexity and information processing within the system, reflecting organized and predictive neural behavior by accounting for temporal correlations in the data. In contrast, LZc is more sensitive to randomness, measuring the diversity and unpredictability of patterns within the data. This distinction allows SC to serve as a more reliable indicator of organized information processing, while LZc highlights the variability in neural signals. Notably, the study reveals that states of higher consciousness are associated with greater complexity, supporting the entropic brain hypothesis. This research contributes to the ongoing efforts to quantify consciousness through mathematical frameworks and offers insights into the neural correlates of different states of awareness.
Keywords
complexity measures; states of consciousness; time series; dynamical systems
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
