Explainable AI-Driven EEG Channel Selection for Accurate and Interpretable Epilepsy Diagnosis

Epilepsy is a complex neurological disorder characterized by recurrent seizures, requiring accurate and timely diagnosis to ensure effective clinical management. This is particularly important in Brain-Computer Interfaces (BCIs), where neurological monitoring plays a key role in assistive and diagnostic applications. In this context, Electroencephalography (EEG) is the primary diagnostic modality; however, traditional manual analysis is labor-intensive, subjective, and prone to inter-expert variability. Automated machine learning techniques offer a promising alternative to manual EEG analysis. The opaque, “black-box” nature of many Machine Learning (ML) models often raise concerns about their reliability in clinical settings, which can hinder their practical implementation. To tackle this issue, Explainable Artificial Intelligence (XAI) has emerged as a transformative solution, providing greater transparency and shedding light on the critical features driving model predictions. In this study, we introduce Explainable LIMEbased Selection of EEG Electrodes (XLISEE), a novel approach that not only enhances EEG channel selection but also strengthens interpretability by leveraging XAI methodologies. The suggested approach is evaluated using the dataset provided by Guinea-Bissau Hospital. The most important EEG channels are identified by XLISEE to differentiate between epileptics and healthy subjects, reducing data complexity without sacrificing the precision of the diagnosis. With accuracy rates of 99.76% for both the Support Vector Machine (SVM) and k-Nearest Neighbor (kNN) classifiers, our experimental results show that the suggested method delivers remarkable classification performance. It also attains 100% accuracy, specificity, sensitivity, and F1-score, as well as a Matthews Correlation Coefficient (MCC) of 1.0. Moreover, lower computational demands improve efficiency, making real-time epilepsy detection more practical and feasible for clinical use. Beyond performance gains, integrating XAI enhances clinician confidence by offering interpretable, evidence-based insights, promoting wider adoption of AI-driven diagnostics.