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

ABSTRACT

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.