Neural complexity -- Statistical-mechanical approach of human electroencephalograms
Abstract
The brain is a complex system whose understanding enables potentially deeper approaches to mental phenomena. Dynamics of wide classes of complex systems have been satisfactorily described within -statistics, a current generalization of Boltzmann-Gibbs (BG) statistics. Here, we study human electroencephalograms of typical human adults (EEG), very specifically their inter-occurrence times across an arbitrarily chosen threshold of the signal (observed, for instance, at the midparietal location in scalp). The distributions of these inter-occurrence times differ from those usually emerging within BG statistical mechanics. They are instead well approached within the -statistical theory, based on non-additive entropies characterized by the index . The present method points towards a suitable tool for quantitatively accessing brain complexity, thus potentially opening useful studies of the properties of both typical and altered brain physiology.
Cite
@article{arxiv.2303.03128,
title = {Neural complexity -- Statistical-mechanical approach of human electroencephalograms},
author = {Dimitri Marques Abramov and Constantino Tsallis and Henrique Santos Lima},
journal= {arXiv preprint arXiv:2303.03128},
year = {2023}
}