Unified Multi-Class Electroencephalogram Artifact Recognition Using Machine Learning Classifiers

Artifacts are noisy signals that commonly contaminate electroencephalographic (EEG) recordings, mixing with underlying brain activity and degrading the quality of neurophysiological data. Previous research on epileptic Anomaly Detection has shown that this approach is also sensitive to unlabelled artifacts, often leading to an increased False Positive rate. While most methods focus on detecting or removing a single type of artifact, this work proposes a unified multi-class framework to classify several artifact types alongside normal and pathological brain activity within a recording using a single, simple Machine Learning classifier. A set of spectral, temporal and statistical features commonly associated with different artifact types is extracted. KNN and XGBoost classifiers are trained and evaluated under a cross-validation scheme. The results demonstrate strong performance for both models, achieving approximately 90% sensitivity across all classes while maintaining a 100% specificity. These findings highlight the effectiveness of a unified and interpretable approach for multi-class EEG artifact classification.