Resting state is not actually a state of rest, as confirmed by the loss of physiological complexity in brain dynamics

Year 2026, Volume: 17 Issue: 57 , 10 - 18 , 16.04.2026

Hasan Fehmi Özel , Hasan Kazdağlı

https://doi.org/10.17944/interdiscip.1676000

https://izlik.org/JA48SZ53JZ

Abstract

Objective: The human brain operates with complex, non-linear dynamics that enable adaptability to cognitive demands. Physiological complexity, measured through electroencephalography (EEG), provides insights into neural organization and function. This study examines how cognitive load induced by verbal mental tasks affects brain complexity using fractal analysis.
Method: EEG data from 36 healthy young adults (9 males and 27 females, aged 18–26 years) were obtained from the publicly available PhysioNet database. These recordings were collected with prior ethical approval and informed consent, and no new data were acquired for the present study. Participants completed a resting-state session followed by a mental arithmetic task with verbal commands. Detrended fluctuation analysis (DFA) was employed to assess EEG complexity, and a novel domain-based complexity loss parameter (dS) was introduced to quantify deviations from an idealized reference. Statistical comparisons were conducted using two-way ANOVA with Šídák correction, and all analyses were performed using GraphPad Prism version 10 (GraphPad Software, San Diego, CA, USA).
Results: Cognitive load led to a significant reduction in DFA values, particularly in the temporal and frontal regions, indicating decreased physiological complexity. dS values increased significantly in the temporal regions, supporting the hypothesis that cognitive demand alters neural dynamics. These findings align with the default mode network concept, highlighting a shift from a high-complexity resting state to a more structured and synchronized state under cognitive load.
Conclusion: The results suggest that physiological complexity decreases during verbal cognitive tasks, with the strongest effects in temporal regions. This supports the use of EEG fractal analysis in assessing cognitive workload and neural efficiency. Future studies should explore its applications in clinical and human-computer interaction contexts.

Keywords

EEG , physiological complexity , cognitive load , fractal analysis , DFA , mental workload , verbal tasks

Ethical Statement

Since this study is conducted with open database, ethics committee approval is not required, and the Helsinki Declaration rules were followed to conduct this study.

Thanks

We would like to thank Zyma et al for sharing their database.

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