NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis
Seizure diagnosis from EEG signals is a critical yet persistently challenging task, due to the complicated neural dynamics and the spurious connections in inter-channel modeling. While spatial-temporal graph neural networks (STGNNs) have advanced EEG brain network representation learning, the resulting graph structures suffer from low clinical plausibility and limited interpretability due to their purely data-driven nature. To this end, we introduce NeuroGRIP, a retrieval-augmented graph refinem
Record details
Published: 15 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 18 July 2026
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ethics.ai (15 July 2026), “NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis,” evidence record 11629, https://ethics.ai/record/11629 (originally published by arXiv cs.LG).
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