EEG Lempel-Ziv Complexity vis-à-vis Autocorrelation Function
Abstract
Measures of signal complexity have become increasingly popular in electroencephalography (EEG), where they are often interpreted as indicators of neural information processing, dynamical richness, or conscious state. In contrast, classical measures of temporal dependence, such as the autocorrelation function (ACF), remain more directly linked to known physiological mechanisms and are easier to interpret. The present work revisits, for the EEG practicioner, the relation between normalized Lempel-Ziv complexity (LZC) and temporal autocorrelation in both synthetic and physiological EEG signals. First, synthetic time series exhibiting a broad range of temporal structure and complexity levels were generated using stochastic processes with varying degrees of temporal dependence. LZC and autocorrelation measures were then computed and compared. Across simulated signals, a robust inverse relationship was observed between normalized LZC and the first autocorrelation coefficient, indicating that signals with stronger temporal correlations exhibit lower complexity estimates. This relationship also holds in resting-state EEG recordings from healthy adult participants across channels and subjects, as well as dynamically during eyes open/close maneuvers. Overall, these results show that the information captured by LZC reflects temporal redundancy already quantified by conventional autocorrelation measures. Because autocorrelation measures relate more directly to established neurophysiological mechanisms, including oscillatory persistence, network synchronization, and temporal integration, these results suggest that interpreting complexity-based EEG findings should explicitly account for their dependence on conventional temporal statistics, which may provide a more transparent framework for mechanistic inferences.
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BibTeX
@misc{montoya2026lempelziv,
title = {{EEG Lempel-Ziv Complexity vis-à-vis Autocorrelation Function}},
author = {Pedro Montoya and Ignacio Cifre and José Luis Rosselló Sanz and Dante R. Chialvo},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.23231},
eprint = {2610.23231},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.23231},
}
RIS
TY - UNPB
TI - EEG Lempel-Ziv Complexity vis-à-vis Autocorrelation Function
AU - Pedro Montoya
AU - Ignacio Cifre
AU - José Luis Rosselló Sanz
AU - Dante R. Chialvo
PY - 2026
DA - 2026/10/16
PB - arXiv
DO - 10.48550/arxiv.2610.23231
UR - https://arxiv.org/abs/2610.23231
AB - Measures of signal complexity have become increasingly popular in electroencephalography (EEG), where they are often interpreted as indicators of neural information processing, dynamical richness, or conscious state. In contrast, classical measures of temporal dependence, such as the autocorrelation function (ACF), remain more directly linked to known physiological mechanisms and are easier to interpret. The present work revisits, for the EEG practicioner, the relation between normalized Lempel-Ziv complexity (LZC) and temporal autocorrelation in both synthetic and physiological EEG signals. First, synthetic time series exhibiting a broad range of temporal structure and complexity levels were generated using stochastic processes with varying degrees of temporal dependence. LZC and autocorrelation measures were then computed and compared. Across simulated signals, a robust inverse relationship was observed between normalized LZC and the first autocorrelation coefficient, indicating that signals with stronger temporal correlations exhibit lower complexity estimates. This relationship also holds in resting-state EEG recordings from healthy adult participants across channels and subjects, as well as dynamically during eyes open/close maneuvers. Overall, these results show that the information captured by LZC reflects temporal redundancy already quantified by conventional autocorrelation measures. Because autocorrelation measures relate more directly to established neurophysiological mechanisms, including oscillatory persistence, network synchronization, and temporal integration, these results suggest that interpreting complexity-based EEG findings should explicitly account for their dependence on conventional temporal statistics, which may provide a more transparent framework for mechanistic inferences.
ER -