Dynamic reconstruction of electroencephalogram data using RBF neural networks

• DOI: 10.3389/fnins.2025.1557763
• Publication Date: 2025-03-28T07:53:21Z
• Authors: Xuan Wang; Congcong Du; Xianjin Ke; Jian Zhang; Zheng Zheng; Yayan Yue; Ming Yu

Electroencephalography (EEG) is widely used for analyzing brain activity; however, the nonlinear and nature of EEG signals presents significant challenges traditional analysis methods. Machine has shown great promise in addressing these limitations. This study proposes a novel approach using Radial Function (RBF) neural networks optimized by Particle Swarm Optimization (PSO) to reconstruct dynamics extract age-related characteristics. recordings were collected from 142 participants spanning multiple age groups. Signals preprocessed through bandpass filtering (1-35 Hz) Independent Component Analysis (ICA) artifact removal. network was trained on time-series data with PSO employed optimize model parameters identify fixed points reconstructed system. Statistical analyses including ANOVA Kruskal-Wallis tests performed assess differences fixed-point coordinates. The RBF demonstrated high accuracy signal reconstruction across different frequency normalized root mean square error (NRMSE) 0.0671 ± 0.0074 Pearson correlation coefficient 0.0678. Spectral time-frequency confirmed s capability accurately capture oscillations. Importantly coordinates revealed distinct age-related. These findings suggest that can serve as quantitative markers aging providing new insights into age-dependent changes dynamics. proposed method offers computationally efficient interpretable potential applications neurological diagnosis cognitive research.