[Paper Review] Dynamic Spatio-Temporal Graph Neural Network for Early Detection of Pornography Addiction in Adolescents Based on Electroencephalogram Signals

Achmad Ardani Prasha, Clavino Ourizqi Rachmadi+4 more|arXiv (Cornell University)|Feb 28, 2026

Sexuality, Behavior, and Technology0 citations

TL;DR

The paper introduces DST-GNN, a dynamic graph neural network using PLI-based spatial graphs and BiGRU temporal modeling to detect pornography addiction in adolescents from EEG data, achieving notable improvements over baselines.

ABSTRACT

Adolescent pornography addiction requires early detection based on objective neurobiological biomarkers because self-report is prone to subjective bias due to social stigma. Conventional machine learning has not been able to model dynamic functional connectivity of the brain that fluctuates temporally during addictive stimulus exposure. This study proposes a state-of-the-art Dynamic Spatio-Temporal Graph Neural Network (DST-GNN) that integrates Phase Lag Index (PLI)-based Graph Attention Network (GAT) for spatial modeling and Bidirectional Gated Recurrent Unit (BiGRU) for temporal dynamics. The dataset consists of 14 adolescents (7 addicted, 7 healthy) with 19-channel EEG across 9 experimental conditions. Leave-One-Subject-Out Cross Validation (LOSO-CV) evaluation shows F1-Score of 71.00%$\pm$12.10% and recall of 85.71%, a 104% improvement compared to baseline. Ablation study confirms temporal contribution of 21% and PLI graph construction of 57%. Frontal-central regions (Fz, Cz, C3, C4) are identified as dominant biomarkers with Beta contribution of 58.9% and Hjorth of 31.2%, while Cz-T7 connectivity is consistent as a trait-level biomarker for objective screening.

Motivation & Objective

• Motivate the need for objective biomarkers to detect adolescent pornography addiction due to self-report biases.
• Develop a DST-GNN architecture that captures dynamic brain functional connectivity from EEG.
• Identify discriminative brain regions and connectivity patterns as biomarkers for addiction.
• Evaluate DST-GNN against baselines using LOSO-CV on an adolescent EEG dataset.
• Provide explainability analyses to relate model decisions to neurobiological markers.

Proposed method

• Construct spatial graphs from EEG via Phase Lag Index (PLI) to model functional connectivity.
• Encode spatial structure with Graph Attention Network (GAT) layers.
• Capture temporal dynamics with Bidirectional GRU (BiGRU) over graph sequences.
• Classify using a Multi-Layer Perceptron on DST-GNN outputs.
• Use LOSO-CV with multiple seeds for robust evaluation.
• Perform ablation and explainability analyses to validate components and biomarkers.

Experimental results

Research questions

• RQ1 Can a dynamic spatio-temporal graph neural network reliably detect pornography addiction from adolescent EEG data?
• RQ2 Which brain regions and connectivity patterns most discriminate addicted from non-addicted adolescents?
• RQ3 How do temporal dynamics and PLI-based connectivity contribute to classification performance?
• RQ4 Is there a trait-level biomarker for pornography addiction independent of task conditions?

Key findings

• DST-GNN achieves an average F1-Score of 71.00% (±12.10%) with recall 85.71% across seeds, outperforming baselines by 104%.
• Temporal modeling (BiGRU) contributes about 21% to performance, and PLI-based graph construction contributes about 57%.
• Frontal-central regions (Fz, Cz, C3, C4) and Cz–T7 connectivity emerge as dominant biomarkers for screening.
• Beta band power and Hjorth features are the most influential for classification, with Beta contributing 58.9% and Hjorth 31.2%.
• Trait-level connectivity, notably Cz–T7, shows consistent differences across conditions, suggesting potential stimulus-independent biomarkers.
• DST-GNN with multi-seed LOSO-CV shows high recall (85.71%) indicating strong screening capability.