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This paper introduces the Sample-Adaptive Hyperbolic Graph Neural Network (SA-HGNN), designed to enhance EEG-based depression recognition by accurately modeling the hierarchical structure of brain networks affected by depression. By employing a Sample-Adaptive Graph Construction module, hyperbolic graph convolution, and an Attention Pooling module, the model effectively captures complex spatial relationships and mitigates noise interference in EEG signals. Extensive experiments reveal that SA-HGNN outperforms existing methods in recognizing abnormal functional connectivity patterns, demonstrating its robustness and efficacy in clinical applications.
Hyperbolic geometry in GNNs can significantly improve EEG-based depression recognition by accurately modeling the brain's hierarchical structure.
Graph Neural Networks (GNNs) have been widely used to capture spatial functional connectivity patterns to improve electroencephalography (EEG)-based depression recognition performance. However, the functional connectivity of brain networks in patients with depression exhibits an inherent hierarchical structure, making it difficult to capture accurate connection patterns. To address these issues, this paper proposes a novel model named Sample-Adaptive Hyperbolic Graph Neural Network (SA-HGNN), which aims to accurately extract the authentic hierarchical structure of depression-affected brain networks. Specifically, the proposed model comprises three core modules. First, a Sample-Adaptive Graph Construction module dynamically constructs personalized brain network topologies to capture more complex spatial relationships within the brain network. Second, hyperbolic graph convolution is employed to overcome the representation bottlenecks of Euclidean space, leveraging hyperbolic geometry to precisely capture latent hierarchical relationships within the brain network. Finally, an Attention Pooling module adaptively filters out highly redundant noise channels in EEG signals, effectively mitigating the interference of inherent noise on the authentic hierarchical topology. Extensive experiments on public EEG datasets demonstrate the superior performance of our method across resting-state and task-related paradigms, validating its robustness to noise and efficacy in capturing abnormal functional connectivity patterns in brain networks of patients with depression.
