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

TL;DR

This paper introduces a novel dynamic spatio-temporal graph neural network that effectively detects adolescent pornography addiction from EEG signals, outperforming traditional methods by modeling brain connectivity fluctuations.

Contribution

It presents a new DST-GNN model combining PLI-based GAT and BiGRU for early addiction detection, capturing dynamic brain connectivity patterns.

Findings

Achieved 71% F1-Score with 85.7% recall, 104% improvement over baseline.

Identified frontal-central EEG regions as key biomarkers.

Demonstrated the importance of temporal dynamics and PLI graphs in detection.

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.