Quantification of brain state into configuration entropy and complexity

Abstract

An objective measure of conscious and unconscious brain states is highly needed in the diagnosis of disorders of consciousness and in advancing the science of consciousness. However, its measurement based on the physical mechanism underlying its occurrence is still challenging. Our study aimed to quantify the electroencephalography (EEG) recording data under conscious state (awake, baseline and no seizure) and reduced consciousness or awareness (sleep, moderate sedation, seizure) into configuration entropy, entropy-complexity, and Gibbs-inspired free-energy. The study utilized the EEG datasets from public open-source repositories for analysis. The datasets were analyzed by using configuration entropy, permutation entropy, Lempel-Ziv complexity and Gibbs' formulae. Our findings revealed that the full waking (awake, baseline, no seizure) states exhibit significantly higher configuration entropy and entropy-complexity than states associated with reduced consciousness or awareness (sleep, sedation, and seizure). This suggests that the conscious brain state contains rich information capacity and is sustained by collective activity of neurons. We also find that the conscious state is associated with a low Gibbs-inspired free-energy index compared to the reduced state of consciousness or awareness. The reduced free-energy index enhances the formation of ordered pattern functional networks, which are essential in maintaining conscious awareness.