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EEG-SeeGraph: Interpreting functional connectivity disruptions in dementias via sparse-explanatory dynamic EEG-graph learning

Fengcheng Wu, Zhenxi Song, Guoyang Xu, Kaisong Hu, Zirui Wang, Yi Guo, Zhiguo Zhang

Abstract

Robust and interpretable dementia diagnosis from noisy, non-stationary electroencephalography (EEG) is clinically essential yet remains challenging. To this end, we propose SeeGraph, a Sparse-Explanatory dynamic EEG-graph network that models time-evolving functional connectivity and employs a node-guided sparse edge mask to reveal the connections that drive diagnostic decisions, while remaining robust to noise and cross-site variability. SeeGraph comprises four components: (1) a dual-trajectory temporal encoder that models dynamic EEG with two streams, where node signals capture regional oscillations and edge signals capture interregional coupling; (2) a topology-aware positional encoder that derives graph-spectral Laplacian coordinates from the fused connectivity and augments node embeddings; (3) a node-guided sparse explanatory edge mask that gates the connectivity into a compact subgraph; and (4) a gated graph predictor that operates on the sparsified graph. The framework is trained using cross-entropy loss together with a sparsity regularizer on the mask, yielding noise-robust and interpretable diagnoses. The effectiveness of SeeGraph is validated on public and in-house EEG cohorts, including patients with neurodegenerative dementias and healthy controls, under both raw and noise-perturbed conditions. Its sparse, node-guided explanations highlight disease-relevant connections and align with established clinical findings on functional connectivity alterations, thereby offering transparent cues for neurological evaluation.

EEG-SeeGraph: Interpreting functional connectivity disruptions in dementias via sparse-explanatory dynamic EEG-graph learning

Abstract

Robust and interpretable dementia diagnosis from noisy, non-stationary electroencephalography (EEG) is clinically essential yet remains challenging. To this end, we propose SeeGraph, a Sparse-Explanatory dynamic EEG-graph network that models time-evolving functional connectivity and employs a node-guided sparse edge mask to reveal the connections that drive diagnostic decisions, while remaining robust to noise and cross-site variability. SeeGraph comprises four components: (1) a dual-trajectory temporal encoder that models dynamic EEG with two streams, where node signals capture regional oscillations and edge signals capture interregional coupling; (2) a topology-aware positional encoder that derives graph-spectral Laplacian coordinates from the fused connectivity and augments node embeddings; (3) a node-guided sparse explanatory edge mask that gates the connectivity into a compact subgraph; and (4) a gated graph predictor that operates on the sparsified graph. The framework is trained using cross-entropy loss together with a sparsity regularizer on the mask, yielding noise-robust and interpretable diagnoses. The effectiveness of SeeGraph is validated on public and in-house EEG cohorts, including patients with neurodegenerative dementias and healthy controls, under both raw and noise-perturbed conditions. Its sparse, node-guided explanations highlight disease-relevant connections and align with established clinical findings on functional connectivity alterations, thereby offering transparent cues for neurological evaluation.
Paper Structure (10 sections, 10 equations, 2 figures, 3 tables)

This paper contains 10 sections, 10 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. METHOD
  3. Dual-Trajectory Temporal Encoder
  4. Topology-Aware Positional Encoder
  5. Node-Guided Sparse Explanatory Connectivity Mask
  6. Gated Graph Predictor
  7. EXPERIMENTS AND RESULTS
  8. Experimental Setup
  9. Experimental Results
  10. Conclusion

Figures (2)

  • Figure 1: Architecture of SeeGraph: dynamic EEG graphs are constructed, node and connectivity trajectories are temporally encoded, Laplacian positional encodings augment node embeddings, and a node-guided sparse mask yields an explanatory subgraph for a gated graph predictor. The objective combines diagnosis cross-entropy with a sparsity regularizer on the edge mask to learn compact, interpretable connectivity.
  • Figure 2: SeeGraph localizes salient fronto-temporal subnetworks via a node-guided sparse mask. AD exhibits temporal-centered ipsilateral clustering (red), while HC shows more interhemispheric and bilateral integration (blue); darker colors indicate higher salience.