EEG Lempel-Ziv Complexity vis-à-vis Autocorrelation Function

Pedro Montoya; Ignacio Cifre; José Luis Rosselló Sanz; Dante R. Chialvo

Home/Archive/arXiv

arXiv · 2610.23231

EEG Lempel-Ziv Complexity vis-à-vis Autocorrelation Function

Pedro Montoya·Ignacio Cifre·José Luis Rosselló Sanz·Dante R. Chialvo

View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

q-bio.NC cond-mat.dis-nn

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Pedro Montoya, Ignacio Cifre, José Luis Rosselló Sanz, Dante R. Chialvo. 2026-10-16. EEG Lempel-Ziv Complexity vis-à-vis Autocorrelation Function. https://arxiv.org/abs/2610.23231

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

PyGPLA: A Python Package for Generalized Phase Locking Analysis

Spike-field couplings measure the links between neuronal spiking and local field potential (LFP) and provide a tool to study how neural populations coordinate activity across spatial and temporal scales. While modern high-density electrophysiology enables the simultaneous recording of numerous LFP channels and neuronal units, this massive scale poses significant statistical and computational challenges for conventional pairwise coupling analyses. The number of channel-unit comparisons grows rapidly, and a coherent population-level structure can be obscured by high-dimensional coupling matrices. Generalized Phase Locking Analysis (GPLA) addresses this challenge by organizing spike-triggered LFP phase information into a complex coupling matrix and extracting dominant low-rank modes. These modes summarize whether a population is coupled to the field, which channels and units contribute to the coupling, and how their relative phases are organized. Here, we present PyGPLA, an open-source Python package implementing GPLA for high-density electrophysiological recordings. Designed for straightforward integration into existing neural data analysis pipelines, PyGPLA supports multivariate spike-LFP coupling analysis, from coupling estimation and interpretation to significance assessment using analytical and surrogate-based tests.

q-bio.NC↗

arXiv2026

Affective Valence as the Interoceptive Rate of Change of Jacobian Scale

Affect is widely held to depend on interoception. This paper proposes that valence is the rate at which interoception moves a self-sustaining system's responsiveness relative to its terminal set (roughly, death). Responsiveness is formalised as the scale of the system's Jacobian and interoception as its internal inputs: receptors, signals and forecasts of its energy reserve. Changes in responsiveness that bypass interoception, such as brain damage, are not felt. Language models have no valence because they are not self-sustaining. The chain rule splits valence over the system's parts and the causes that affect its internal inputs. With the log scale, these splits account for need-dependent pleasure, diffuse hunger and localised pain, negativity bias, and relief when a threat fails to arrive. In a minimal model of a body: (1) Blocking the reserve signal leaves depletion unfelt, and driving it produces hunger with an undepleted reserve. (2) Removing receptors or their gain, or driving receptors, reproduces congenital insensitivity to pain, asymbolia and phantom pain. (3) A receptor gain learned to sustain the body signals local damage to the whole body, so an injury hurts in proportion to its cost. (4) If the reserve also modulates the gain, the body learns to suppress pain due to receptors as energy runs low. (5) When it learns to maximise its own scale, it becomes a wirehead. The account explains when changes are felt, where they are felt, and when feelings mislead.

q-bio.NC↗

arXiv2026

Integrative neurocybernetic modeling in the era of large-scale neuroscience

Large-scale neuroscience is generating rich datasets across animals, brain areas, and behavioral contexts, yet our modeling efforts remain fragmented across isolated experiments. We argue that understanding behavior requires integrative neurocybernetic models: understandable dynamical models that capture the closed-loop coupling of brain, body, and environment, treat the brain as a controller pursuing latent objectives, represent structured variation across scales, and scale to heterogeneous datasets. Such models shift the goal from predicting neural recordings in isolation to inferring the organizing principles that govern neural and behavioral dynamics. We outline a practical route toward this goal by combining nonlinear state-space models and meta-dynamical extensions with scalable inference, knowledge distillation, and mixed open- and closed-loop training. By pooling complementary constraints from recordings, behavior, perturbations, and anatomy, integrative neurocybernetic models can provide statistical amplification, few-shot generalization, and mechanistic insight into shared dynamical structure, individual variation, and the control objectives that govern behavior. This agenda offers a model-centric path from fragmented data to a mechanistic science of how brains produce behavior.

q-bio.NC↗