Automatic Epileptic Seizure Detection with MUSIC and Cross-Correlation Methods: Performance Enhancement with Ensemble Learning-Voting

Epileptic seizures are characterized by abnormal neuronal discharges that generate distinctive patterns in EEG signals, requiring accurate and fast detection for clinical decision support. This study proposes a high-resolution spectral approach that integrates the Multiple Signal Classification (MUSIC) algorithm with cross-correlation-based feature extraction for automated seizure detection. High-resolution spectral estimates of reference EEG signals and individual segments were obtained using the MUSIC algorithm, and six correlation-driven statistical features were computed to capture both spectral similarity and phase relationships. These features were classified using Random Forest, k-Nearest Neighbor, Multilayer Perceptron, and an Ensemble Learning-Voting model. Experiments were conducted on the Bonn University EEG dataset across 14 binary and multi-class tasks. The Ensemble Learning-Voting classifier achieved the best overall performance with an average accuracy of 99.17%, outperforming individual classifiers. The proposed methodology provides high frequency resolution, low computational cost, and robust classification capability, demonstrating strong potential for real-time epileptic seizure detection and integration into clinical EEG monitoring systems.