https://doi.org/10.1140/epjs/s11734-025-01509-y
Regular Article
Recurrence quantification analysis and theta-band functional networks detect age-related changes in brain sensorimotor system: VR-based approach
Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, A. Nevskogo, 14, 236041, Kaliningrad, Russia
Received:
10
December
2024
Accepted:
6
February
2025
Published online:
13
February
2025
In this paper, we present the results of spectral, complexity and functional connectivity analysis of EEG signals recorded during VR-based experimental session in two groups of subjects. We focus on the analysis of aging biomarkers associated with theta-rhythm alterations associated with sensorimotor task performance. Recurrence quantification analysis-based measures of EEG complexity show the potential to reveal the aging indicators that are otherwise omitted by the conventional spectral power analysis, demonstrating that the group of elderly subjects induce the neural activity associated with working memory load more actively during the sequential sensorimotor integration task performance. The RQA is supplemented by the functional connectivity analysis, revealing the hyper-compensation in the eldelry group of subjects.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
