SA-HGNN: Sample-Adaptive Hyperbolic Graph Neural Network for EEG-Based Depression Recognition
arXiv:2607.02063v1 Announce Type: new Abstract: 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 hierarc
This research is emerging as AI, particularly GNNs, becomes more sophisticated in processing complex biological data such as EEG signals for diagnostic purposes.
Improved EEG-based depression recognition could lead to earlier, more accurate diagnoses and personalized treatment approaches, leveraging advanced AI techniques.
The proposed SA-HGNN model offers a new method for analyzing brain network functional connectivity, potentially enhancing the accuracy of depression recognition beyond existing GNN methods.
- · Neuroscience researchers
- · Mental health diagnostics
- · AI in healthcare
- · Patients with depression
- · Traditional diagnostic methods
- · Inaccurate GNN models (if replaced)
- · Companies relying on less sophisticated EEG analysis
More precise and earlier diagnosis of depression becomes feasible.
Development of targeted therapies and interventions based on refined neurological insights accelerates.
The integration of AI into clinical neurological assessment becomes a new standard, impacting healthcare infrastructure and professional training.
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Read at arXiv cs.LG