A Hybrid Multi-Domain Feature Fusion and Ensemble Learning Framework for Neurodynamic Analysis of Focal and Non-Focal EEG Signals

Abstract

Accurate differentiation between focal and non-focal EEG activity remains a challenging task due to the nonlinear and nonstationary nature of neural signals. This work proposes a novel hybrid framework that combines multi-stage signal filtering, multi-domain feature extraction, and ensemble learning to improve automated neurodynamic analysis. The framework employs a dual-path design: the first path extracts linear, nonlinear, and hybrid signal descriptors, while the second path generates dimensionality-reduced representations that are fused with these descriptors to form a comprehensive, information-rich feature set. Performance was rigorously evaluated across eight machine learning paradigms, including conventional, ensemble, and hybrid models, ensuring methodological rigor and reproducibility. Results demonstrate that fusing nonlinear features with reduced-dimensional embeddings, when processed through a stacked ensemble classifier, achieves superior discrimination between focal and non-focal EEG patterns. The framework's scalability, computational efficiency, and adaptability establish a robust foundation for automated EEG-based neurodynamic analysis, offering strong potential for future studies in cognitive signal processing, neural dynamics modeling, and epilepsy research.