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

TL;DR

EEG-MedRAG introduces a hypergraph-based retrieval-augmented generation framework that improves semantic interpretation and diagnostic accuracy of EEG data in clinical settings, supported by a new cross-disease EEG QA benchmark.

Contribution

The paper presents a novel hypergraph retrieval-augmented generation model for EEG data and introduces the first cross-disease EEG clinical QA benchmark.

Findings

EEG-MedRAG outperforms existing models in answer accuracy and retrieval.

The framework enables joint semantic-temporal retrieval and causal diagnostic generation.

The benchmark facilitates systematic evaluation of disease-agnostic and role-aware EEG understanding.

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.