Quantifying the Generalization Gap in Seizure Detection: A Large-Scale Empirical Benchmark via the SzCORE Challenge

Seizure detection using long-term electroencephalography (EEG) is critical for patient care but remains unreliable due to models that do not generalize well across diverse patient populations. The SzCORE Challenge was created to empirically evaluate and benchmark various seizure detection models, focusing on their performance across different clinical settings.

Participants submitted their models, which were rigorously tested on a large dataset of EEG recordings from multiple patients. Findings revealed that while some models achieved high accuracy in training environments, they often exhibited a substantial generalization gap when faced with unseen patient data.

For instance, models that performed well in one demographic struggled significantly in another, highlighting the need for more robust training methodologies. These results underscore the importance of standardized evaluation metrics in developing reliable seizure detection systems.

Engineers and researchers can leverage these insights to improve model robustness and ensure better clinical applicability.