https://doi.org/10.1140/epjs/s11734-024-01453-3
Regular Article
Frontal long-range temporal correlations as a predictor of child’s IQ test performance using machine learning approach
Immanuel Kant Baltic Federal University, A. Nevskogo Str. 14, 236016, Kaliningrad, Russia
Received:
30
October
2024
Accepted:
12
December
2024
Published online:
7
January
2025
This study explores the relationship between long-range temporal correlations in brain activity, measured through detrended fluctuation analysis of electroencephalogram signals, and performance on an intelligence test (Raven’s Progressive Matrices) in children. Specifically, the research focuses on identifying reliable neurophysiological markers of cognitive functions by analysing EEG data from school-aged children (8–10 years old) in a resting state. The DFA scaling factor in the alpha range of the frontal cortex was found to be a significant predictor of RPM performance, with results validated using machine learning methods. These results highlight the importance of long-range temporal correlations in brain activity as a potential neurophysiological marker for assessing cognitive abilities.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024
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.
