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This paper introduces SpaTeoGL, a spatiotemporal graph learning framework that constructs window-level spatial graphs of iEEG electrode interactions and a temporal graph linking time windows based on spatial graph similarity. The method uses a smooth graph signal processing formulation solved via alternating block coordinate descent, providing convergence guarantees. Experiments on a multicenter iEEG dataset demonstrate that SpaTeoGL achieves competitive SOZ localization performance compared to horizontal visibility graphs and logistic regression, while also enhancing non-SOZ identification and offering interpretable insights into seizure dynamics.
Unlocking interpretable seizure onset zone analysis, SpaTeoGL learns dynamic brain connectivity patterns from iEEG data, offering insights into seizure propagation that could improve surgical outcomes.
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.