Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged by complex spatiotemporal seizure dynamics. We propose SpaTeoGL, a spatiotemporal graph learning framework for interpretable seizure network analysis. SpaTeoGL jointly learns window-level spatial graphs capturing interactions among iEEG electrodes and a temporal graph linking time windows based on similarity of their spatial structure. The method is formulated within a smooth graph signal processing framework and solved via an alternating block coordinate descent algorithm with convergence guarantees. Experiments on a multicenter iEEG dataset with successful surgical outcomes show that SpaTeoGL is competitive with a baseline based on horizontal visibility graphs and logistic regression, while improving non-SOZ identification and providing interpretable insights into seizure onset and propagation dynamics.

翻译：从颅内脑电图（iEEG）中准确定位癫痫发作起始区（SOZ）对于癫痫手术至关重要，但复杂的时空癫痫发作动力学带来了挑战。我们提出了SpaTeoGL，一个用于可解释的癫痫发作网络分析的时空图学习框架。SpaTeoGL联合学习捕获iEEG电极间相互作用的窗口级空间图，以及基于时间窗口空间结构相似性连接这些窗口的时间图。该方法在平滑图信号处理框架内进行公式化，并通过具有收敛保证的交替块坐标下降算法求解。在一个包含成功手术结果的多中心iEEG数据集上的实验表明，SpaTeoGL与基于水平可见性图和逻辑回归的基线方法性能相当，同时改善了非SOZ的识别，并为癫痫发作起始和传播动力学提供了可解释的洞见。