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 q-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 q-statistical theory, based on non-additive entropies characterized by the index q. 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.

Details

Title
Neural complexity through a nonextensive statistical–mechanical approach of human electroencephalograms
Author
Abramov, Dimitri Marques 1 ; Tsallis, Constantino 2 ; Lima, Henrique Santos 3 

 Fundacao Oswaldo Cruz, Laboratório de Neurobiologia e Neurofisiologia Clínica, Instituto Nacional da Saude da Criança, da Mulher e do Adolescente Fernandes Figueira, Rio de Janeiro, Brazil (GRID:grid.418068.3) (ISNI:0000 0001 0723 0931) 
 Centro Brasileiro de Pesquisas Fisicas and National Institute of Science and Technology for Complex Systems, Rio de Janeiro, Brazil (GRID:grid.418228.5) (ISNI:0000 0004 0643 8134); Santa Fe Institute, Santa Fe, USA (GRID:grid.209665.e) (ISNI:0000 0001 1941 1940); Complexity Science Hub Vienna, Vienna, Austria (GRID:grid.484678.1) 
 Centro Brasileiro de Pesquisas Fisicas and National Institute of Science and Technology for Complex Systems, Rio de Janeiro, Brazil (GRID:grid.418228.5) (ISNI:0000 0004 0643 8134) 
Pages
10318
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2829617276
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.