Dynamic reconstruction of electroencephalogram data using RBF neural networks - Wang, Xuan; Du, Congcong; Ke, Xianjin; Zhang, Jian; Zheng, Zheng; Yue, Yayan; Yu, Ming (Frontiers in Neuroscience, 2025)
IntroductionElectroencephalography (EEG) is widely used for analyzing brain activity; however, the nonlinear and nature of EEG signals presents significant challenges for 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 EEG dynamics and extract age-related neural characteristics.MethodsEEG recordings were collected from 142 participants spanning multiple age groups. Signals were preprocessed through bandpass filtering (1–35 Hz) and Independent Component Analysis (ICA) for artifact removal. neural network was trained on EEG time-series data with PSO employed to optimize model parameters identify fixed points in the reconstructed neural system. Statistical analyses including ANOVA and Kruskal-Wallis tests were performed to assess age-related differences in fixed-point coordinates.ResultsThe RBF network demonstrated high accuracy in EEG signal reconstruction across different frequency a normalized root mean square error (NRMSE) of 0.0671 ± 0.0074 and a Pearson correlation coefficient ± 0.0678. Spectral and time-frequency analyses confirmed the model s capability to accurately capture oscillations. Importantly analysis of RBF network fixed-point coordinates revealed distinct age-related.DiscussionThese findings suggest that fixed-point coordinates of RBF networks can serve as quantitative markers aging providing new insights into age-dependent changes in brain dynamics. The proposed method offers computationally efficient and interpretable approach for EEG analysis with potential applications in neurological diagnosis and cognitive research.