Selection of Low-Density EEG Montages for Optimal Seizure Detection Performance

Multi-channel electroencephalography (EEG) is widely recognised for its ability to non-invasively capture dynamic brain activity with high temporal resolution. However, conventional EEG systems require meticulous setup, expert interpretation and specialised infrastructure, which limits their applicability in real-time, resource-constrained environments such as critical care and emergency settings. As wearable and point-of-care EEG technologies gain traction, there is increasing demand for low-density, automated seizure detection methods that are clinically reliable and scalable. This study presents a data-driven pipeline for an EEG-based task, seizure detection, that identifies compact and clinically relevant four-channel electrode configurations. Using standard 10-20 EEG recordings, the pipeline integrates clinical domain knowledge, preprocessing, feature extraction, and ensemble boosted classification to evaluate seizure detection performance across multiple electrode combinations. The approach was validated on both a heterogeneous local paediatric EEG dataset and an open-source adult dataset. Our results show that several four-channel montages achieve robust patient-independent performance, with central and temporal electrodes frequently appearing in the top-performing configurations. These findings support the feasibility of accurate seizure detection using low-density EEG, even across diverse clinical populations. The primary contribution of this work is a generalisable pipeline for selecting compact EEG montages, which balances domain insight and data-driven optimisation. This framework can be readily adapted to other EEG-based tasks and offers a practical path toward real-time wearable EEG monitoring in resource-constrained environments.