Patient-independent seizure onset zone localization with generalizable feature learning and multi-task supervision.

Most existing ictal stereoelectroencephalography (SEEG)-based seizure onset zone (SOZ) localization methods rely on patient-specific training, limiting their clinical applicability due to the scarcity of seizure recordings and substantial inter-patient variability. Consequently, robust patient-independent SOZ localization remains a major challenge. In this work, we propose a deep learning approach for patient-independent SOZ localization using ictal SEEG recordings, aiming to improve cross-patient generalization while preserving seizure-related temporal characteristics. To mitigate domain shifts across subjects, we introduce a clinically guided feature learning strategy that combines a cross-frequency coupling (CFC) mechanism to capture SOZ-related abnormal interactions across frequency bands with a self-comparison (SC) mechanism to emphasize seizure-onset evolution patterns within SEEG channels. We further incorporate seizure detection as an auxiliary task within a multi-task learning framework to provide seizure-onset-related temporal supervision, thereby improving the temporal awareness and generalizability of SOZ localization. Experiments on the public OpenNeuro HUP dataset demonstrate substantial improvements over existing methods, while additional evaluations on a private clinical dataset further validate the robustness and cross-patient generalization capability of the proposed method. Moreover, comparisons between the learned CFC representations and clinically established phase-amplitude coupling (PAC) metrics reveal consistent physiological patterns, supporting the interpretability of the learned representations.