SpaTeoGL: New Machine Learning Framework for Identifying Seizure Onset Zones in Epilepsy Surgery
Researchers have proposed SpaTeoGL, a spatiotemporal graph learning framework designed to identify seizure onset zones (SOZ) from intracranial EEG recordings in epilepsy patients. The method jointly models spatial interactions among brain electrodes and temporal relationships across time windows, solved via an alternating block coordinate descent algorithm with convergence guarantees. Accurate SOZ localization is critical for guiding epilepsy surgery, and the framework's interpretability could help clinicians better understand seizure onset and propagation dynamics.
SpaTeoGL is a spatiotemporal graph learning framework introduced to address the challenge of accurately localizing seizure onset zones (SOZ) from intracranial EEG (iEEG) data, a key step in planning epilepsy surgery. The method simultaneously learns window-level spatial graphs that capture interactions among iEEG electrodes and a temporal graph that links time windows based on the similarity of their spatial structure. It is formulated within a smooth graph signal processing framework and optimized using an alternating block coordinate descent algorithm, for which convergence is theoretically guaranteed. Experiments were conducted on a multicenter iEEG dataset drawn from patients with successful surgical outcomes, providing a meaningful benchmark. SpaTeoGL performed competitively against a baseline combining horizontal visibility graphs with logistic regression, and notably improved identification of non-SOZ regions. Beyond classification performance, the framework offers interpretable insights into seizure onset and propagation dynamics, which may be valuable for clinical decision-making. The work is a preprint of five pages with four figures, submitted to arXiv in February 2026 and revised in June 2026.
What's missing
The comparison is limited to a single baseline method, leaving open how SpaTeoGL performs against other state-of-the-art SOZ localization approaches. Generalizability to patients with unsuccessful surgical outcomes or diverse epilepsy etiologies is not addressed. As a preprint, the work has not yet undergone formal peer review.
What different sources said
- arXiv cs.LGCenter
SpaTeoGL: Spatiotemporal Graph Learning for Interpretable Seizure Onset Zone Analysis from Intracranial EEG
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