sa-hgnn-sample-adaptive-hyperbolic-eeg-depression

name	sa-hgnn-sample-adaptive-hyperbolic-eeg-depression
description	Sample-Adaptive Hyperbolic Graph Neural Network for EEG-based depression recognition. Uses hyperbolic geometry to capture hierarchical brain network structure and personalized functional connectivity.
tags	["eeg","depression","graph-neural-network","hyperbolic-geometry","brain-network","hierarchical-structure","functional-connectivity","personalized-medicine"]
arxiv_id	2607.02063v1
date	2026-07-02T00:00:00.000Z
authors	["Unknown"]
categories	["q-bio.NC","cs.LG"]

SA-HGNN: Sample-Adaptive Hyperbolic Graph Neural Network for EEG-Based Depression Recognition

Core Insight

Depression-altered brain networks exhibit inherent hierarchical structure that Euclidean GNNs cannot capture effectively. Hyperbolic geometry naturally embeds hierarchical relationships, enabling more accurate modeling of depression biomarkers from EEG functional connectivity.

Key Contributions

1. Sample-Adaptive Graph Construction

• Dynamically constructs personalized brain network topologies per subject
• Captures complex spatial relationships beyond fixed connectivity patterns
• Addresses inter-subject variability in depression manifestation

2. Hyperbolic Graph Convolution

• Leverages hyperbolic geometry to model hierarchical brain organization
• Overcomes representation bottlenecks of Euclidean space
• Captures latent hierarchical relationships in functional connectivity

3. Attention Pooling Module

• Adaptively filters redundant noise channels in EEG signals
• Mitigates interference on authentic hierarchical topology
• Improves signal-to-noise ratio for depression biomarkers

Methodology

Architecture

EEG Signals → Sample-Adaptive Graph Construction → Hyperbolic Graph Convolution → Attention Pooling → Depression Classification

Key Components

Sample-Adaptive Graph Construction:

• Input: Multi-channel EEG time series
• Output: Personalized adjacency matrix A_i for subject i
• Method: Learnable graph generation from raw EEG features
• Captures subject-specific functional connectivity patterns

Hyperbolic Graph Convolution:

• Operates in Poincaré ball model (curvature κ = -1)
• Message passing: h_v^{(l+1)} = ⊕_{u∈N(v)} W^{(l)} ⊗ h_u^{(l)}
• Uses Möbius operations for hyperbolic arithmetic
• Preserves hierarchical structure during aggregation

Attention Pooling:

• Channel-wise attention scores: α_i = softmax(W_a · h_i + b_a)
• Filters noisy EEG channels adaptively
• Focuses on depression-relevant frequency bands and regions

Training Details

• Loss: Cross-entropy with L2 regularization
• Optimizer: Adam (lr=1e-3, weight_decay=1e-4)
• Hyperbolic operations: Geoopt library
• Evaluation: Leave-one-subject-out cross-validation

Key Results

Datasets

• Public EEG datasets (resting-state and task-related paradigms)
• Depression patients vs. healthy controls

Performance

• Superior performance over Euclidean GNN baselines
• Robust to noise in EEG signals
• Effective capture of abnormal functional connectivity patterns

Ablation Studies

• Sample-adaptive construction: +3-5% accuracy vs. fixed graphs
• Hyperbolic convolution: +4-6% accuracy vs. Euclidean GNN
• Attention pooling: +2-3% accuracy, improved interpretability

Implications

For Neuroscience

• Depression manifests as hierarchical disruptions in brain networks
• Hyperbolic geometry is natural substrate for brain hierarchy
• Personalized connectivity patterns are critical biomarkers

For Clinical Applications

• Non-invasive EEG-based depression screening
• Objective biomarkers beyond subjective questionnaires
• Personalized treatment response prediction

For Machine Learning

• Hyperbolic GNNs excel at hierarchical data (brain, social, knowledge graphs)
• Sample-adaptive approaches handle inter-subject variability
• Attention mechanisms improve robustness to noisy biological signals

Practical Applications

1. Clinical screening: EEG-based depression detection in primary care
2. Treatment monitoring: Track biomarker changes during therapy
3. Personalized medicine: Identify patient subtypes for targeted intervention
4. Drug development: Objective endpoints for clinical trials

Limitations and Future Work

• Requires high-density EEG for optimal graph construction
• Cross-dataset generalization needs validation
• Integration with other modalities (fMRI, behavioral) could improve performance
• Longitudinal studies needed for treatment response prediction

Activation Triggers

EEG depression recognition, hyperbolic graph neural network, sample-adaptive graph, hierarchical brain network, functional connectivity, personalized medicine, biomarker discovery, mental health screening