ArticleFrontiers in neuroscience2026
EEG-based stroke severity classification using higher-order topological features and graph convolutional networks.
Article in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Electroencephalography (EEG)-based stroke analysis has mainly relied on conventional signal and network descriptors, while higher-order brain network structures remain insufficiently characterized. Methods: We used persistent homology to extract cycle-based topological features from EEG functional networks, capturing higher-order organization with reduced sensitivity to threshold selection. These features were integrated with conventional EEG representations and embedded into a graph convolutional network for stroke severity classification. Results: The proposed framework achieved 86% accuracy in discriminating mild from moderate stroke. Cycle ratio analysis further revealed that the prefrontal cortex exhibited the most prominent higher-order structures, indicating its prominent involvement in post-stroke brain network organization. Discussion: Our results suggest that higher-order topological features can enhance EEG-based stroke severity classification and offer additional insight into post-stroke brain network alterations.
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