Neurophysiological correlates to the human brain complexity through q-statistical analysis of electroencephalogram

The prospects of assessing neural complexity (NC) by q-statistics of the systemic organization of different types and levels of brain activity were studied. In 70 adult subjects, NC was assessed via the parameter q of q-statistics, applied to the ongoing EEG and of 20 scalp points (channels). The NC were estimated both globally for all channels (AllCh) and locally (for each single channel) in different Functional States (FSs). The values of q was compared among FSs and single channels, as well they were correlated with the power of θ (4-8Hz), [Formula: see text] (15-25Hz) and others EEG bands, in each FS. The value of q across all FSs was higher for AllCh than for the single channels FSs. Consistently with previous studies, we found a negative correlation between NC and age. The FSs did not influence the q of the EEG in AllCh, although locally the FS modulated q in a consistent manner (e.g., reducing q in posterior sites with eyes closed). The q was correlated positively with the power of the θ and negatively with that of the [Formula: see text] band in general. These findings support the idea that, as a first approach, q-statistics seems to be related to the human NC. The relationship between q and θ power aligns with greater NC during FSs such as listening music and resting with eyes open, which is consistent with high-order representations rather than low-informative attentional tasks (OddBall).

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Publication Details

Journal
Scientific Reports
Published
2025-10-20
DOI
https://doi.org/10.1038/s41598-025-21156-6
Citations
1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.43

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article

Neurophysiological correlates to the human brain complexity through q-statistical analysis of electroencephalogram

Dimitri Marques Abramov, Lazarev Vv, Rosiane Ronchi Nascimento Costa, Constantino Tsallis et al.
1 citations
Scientific Reports
EEG and Brain-Computer Interfaces
1.43
article

Neurophysiological correlates to the human brain complexity through q-statistical analysis of electroencephalogram

Dimitri Marques Abramov, Lazarev Vv, Rosiane Ronchi Nascimento Costa, Constantino Tsallis, Henrique Santos Lima, Daniel de Freitas Quintanilha, Carla Kamil-Leite
article en
1 citations

Abstract

The prospects of assessing neural complexity (NC) by q-statistics of the systemic organization of different types and levels of brain activity were studied. In 70 adult subjects, NC was assessed via the parameter q of q-statistics, applied to the ongoing EEG and of 20 scalp points (channels). The NC were estimated both globally for all channels (AllCh) and locally (for each single channel) in different Functional States (FSs). The values of q was compared among FSs and single channels, as well they were correlated with the power of θ (4-8Hz), [Formula: see text] (15-25Hz) and others EEG bands, in each FS. The value of q across all FSs was higher for AllCh than for the single channels FSs. Consistently with previous studies, we found a negative correlation between NC and age. The FSs did not influence the q of the EEG in AllCh, although locally the FS modulated q in a consistent manner (e.g., reducing q in posterior sites with eyes closed). The q was correlated positively with the power of the θ and negatively with that of the [Formula: see text] band in general. These findings support the idea that, as a first approach, q-statistics seems to be related to the human NC. The relationship between q and θ power aligns with greater NC during FSs such as listening music and resting with eyes open, which is consistent with high-order representations rather than low-informative attentional tasks (OddBall).

Scientific ReportsVol. 15(1)
Santa Fe Institute (US), Complexity Science Hub (AT), Centro Brasileiro de Pesquisas Físicas (BR), Instituto Nacional de Saúde (MZ), Fundação Oswaldo Cruz (BR)
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Oswaldo Cruz, Foundation for Scientific and Technological Development in Health
Openalex Percentile: Top 15%
EEG and Brain-Computer Interfaces
1.43
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