Statistical Complexity and Nontrivial Collective Behavior in Electroencephalografic Signals

TL;DR

This study measures the statistical complexity of EEG signals to distinguish healthy individuals from epileptic patients, revealing that higher complexity correlates with healthy brain dynamics and nontrivial collective behavior.

Contribution

It introduces a complexity measure for EEG signals and links it to collective behavior, providing a new criterion for characterizing brain states in health and disease.

Findings

Healthy EEG signals show higher complexity than epileptic signals.

Nontrivial collective behavior correlates with increased complexity.

Epilepsy may involve a loss of dynamical complexity in brain activity.

Abstract

We calculate a measure of statistical complexity from the global dynamics of electroencephalographic (EEG) signals from healthy subjects and epileptic patients, and are able to stablish a criterion to characterize the collective behavior in both groups of individuals. It is found that the collective dynamics of EEG signals possess relative higher values of complexity for healthy subjects in comparison to that for epileptic patients. To interpret these results, we propose a model of a network of coupled chaotic maps where we calculate the complexity as a function of a parameter and relate this measure with the emergence of nontrivial collective behavior in the system. Our results show that the presence of nontrivial collective behavior is associated to high values of complexity; thus suggesting that similar dynamical collective process may take place in the human brain. Our findings also suggest that epilepsy is a degenerative illness related to the loss of complexity in the brain.