Outlier Detection in EEG Signals Using Ensemble Classifiers

Epilepsy is one of the most prevalent neurological disorders, affecting over 50 million people worldwide. Accurate detection and characterization of epileptic activity are clinically critical, as seizures are associated with substantial morbidity, mortality, and impaired quality of life. Electroencephalography (EEG) remains the gold standard for epilepsy assessment; however, its manual interpretation is time-consuming, subjective, and prone to inter-rater variability, emphasizing the need for automated analytical approaches. This study proposes an automated ensemble classification framework for outlier detection in EEG signals. Three interpretable baseline models—Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and decision tree (DT-CART)—were screened. Ensembles were formed only from base models that had a pre-registered meta-selection rule ($F_1$ on the outlier-class $>0.60$). Under this criterion, DT-CART did not qualify and was excluded from all ensembles; final ensembles combined SVM and k-NN. The framework was evaluated on two publicly available datasets with distinct acquisition conditions. The Bonn EEG dataset comprises 500 artifact-free single-channel recordings from healthy subjects and epilepsy patients under controlled laboratory settings. In contrast, the Guinea-Bissau and Nigeria Epilepsy (GBNE) dataset contains multi-channel EEG recordings from 97 participants acquired in field conditions using low-cost equipment, reflecting real-world diagnostic challenges such as motion artifacts and signal variability. The ensemble framework substantially improved outlier detection performance, with stacking achieving up to a 95.0% $F_1$-score (accuracy 95.0%) on the Bonn dataset and 85.5% $F_1$-score (accuracy 85.5%) on the GBNE dataset. These findings demonstrate that the proposed approach provides a robust, interpretable, and generalizable solution for EEG analysis, with strong potential to enhance reliable, efficient, and scalable epilepsy detection in both laboratory and resource-limited clinical environments.