Hybrid fuzzy machine learning models optimized with meta-heuristics for accurate EEG-based neurological assessment

Accurate and timely analysis of electroencephalogram (EEG) signals is critical for the assessment of neurological disorders such as coma and epileptic seizures. Conventional EEG analysis is often time-consuming, prone to human error, and limited by the availability of skilled specialists, highlighting the need for automated, reliable, and intelligent diagnostic systems. This study presents a unified hybrid framework that leverages meta-heuristic optimized machine learning approaches for the classification of EEG signals in multiple neurological conditions. Features were extracted from EEG signals, including time- and frequency-domain characteristics, statistical properties, and nonlinear metrics. Feature mapping and dimensionality reduction were performed using advanced optimization techniques such as Harris Hawks Optimization (HHO) and the Starfish Optimization Algorithm (SFOA), combined with Fuzzy-PCA and Auto-Encryption (AE) for robust feature representation. Classification was conducted using hybrid models including Fuzzy K-NN, FSVM, and DT-FIS, enabling accurate discrimination between different levels of consciousness and stages of epileptic seizures. Experimental results demonstrated high performance, achieving up to 99.53% accuracy for deep coma classification and 99.28% F1-score for seizure detection, with significant improvements in precision, recall, and robustness against feature variability. The proposed framework highlights the efficacy of combining hybrid learning models, fuzzy logic, and meta-heuristic optimization for EEG-based diagnosis, providing a scalable, automated, and highly accurate system for neurological assessment.