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ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Brain Imaging Methods
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1557763
Dynamic Reconstruction of Electroencephalogram Data Using RBF Neural Networks
Provisionally accepted- 1 Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu Province, China
- 2 Beijing Normal University, Beijing, Beijing Municipality, China
AbstractThis study presents a novel approach for electroencephalogram (EEG) analysis using Radial Basis Function (RBF) neural networks optimized by Particle Swarm Optimization (PSO). Analysis of EEG recordings from 142 participants demonstrated the model's robust performance in reconstructing neural dynamics across multiple frequency bands. Notably, analysis of RBF network fixed-point coordinates revealed distinct age-related patterns, with X-coordinates showing significant decreases in the 20-30 age group, while Y-coordinates exhibited nonlinear developmental trends (increasing during adolescence and declining after age 60), collectively providing a new quantitative marker for age-related neural changes. This innovative methodology offers a powerful tool for understanding complex neural dynamics and holds promise for advancing neurological diagnosis and research.
Keywords: Electroencephalogram, RBF neural networks, age-related analysis, Brain Dynamics, Particle Swarm Optimization
Received: 10 Jan 2025; Accepted: 03 Mar 2025.
Copyright: © 2025 Wang, DU, Ke, Zhang, Zheng, Yue and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Ming Yu, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu Province, China
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