EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2508.13735v2
• Date: Sat, 11 Oct 2025 10:36:49 GMT
• Title: EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation
• Authors: Yi Wang, Haoran Luo, Lu Meng, Ziyu Jia, Xinliang Zhou, Qingsong Wen,
• Abstract summary: EEG-MedRAG is a three-layer hypergraph-based retrieval-augmented generation framework.<n>It unifies EEG domain knowledge, individual patient cases, and a large-scale repository into a traversable n-ary relational hypergraph.<n>We introduce the first cross-disease, cross-role EEG clinical QA benchmark, spanning seven disorders and five authentic clinical perspectives.
• Score: 45.031633614714
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the widespread application of electroencephalography (EEG) in neuroscience and clinical practice, efficiently retrieving and semantically interpreting large-scale, multi-source, heterogeneous EEG data has become a pressing challenge. We propose EEG-MedRAG, a three-layer hypergraph-based retrieval-augmented generation framework that unifies EEG domain knowledge, individual patient cases, and a large-scale repository into a traversable n-ary relational hypergraph, enabling joint semantic-temporal retrieval and causal-chain diagnostic generation. Concurrently, we introduce the first cross-disease, cross-role EEG clinical QA benchmark, spanning seven disorders and five authentic clinical perspectives. This benchmark allows systematic evaluation of disease-agnostic generalization and role-aware contextual understanding. Experiments show that EEG-MedRAG significantly outperforms TimeRAG and HyperGraphRAG in answer accuracy and retrieval, highlighting its strong potential for real-world clinical decision support. Our data and code are publicly available at https://github.com/yi9206413-boop/EEG-MedRAG.

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