用脑电图+动态图神经网络，提前识别青少年色情成瘾风险
Dynamic Spatio-Temporal Graph Neural Network for Early Detection of Pornography Addiction in Adolescents Based on Electroencephalogram Signals
- 结合相位滞后指数与双向门控循环单元，建模脑区动态连接变化
- 在14人数据上实现85.7%召回率，较基线提升104%
- 发现额中央脑区和特定脑区连接是关键生物标志物
青少年色情成瘾需基于客观神经生物标志物进行早期检测，因自述易受社会污名影响而存在主观偏差。传统机器学习难以捕捉成瘾刺激暴露过程中脑功能连接的动态变化。本研究提出一种先进的动态时空图神经网络（DST-GNN），融合基于相位滞后指数（PLI）的图注意力网络（GAT）进行空间建模，以及双向门控循环单元（BiGRU）建模时间动态。数据集包含14名青少年（7名成瘾者，7名健康对照）在9种实验条件下采集的19通道脑电信号。采用留一被试交叉验证（LOSO-CV）评估，获得F1分数71.00%±12.10%和召回率85.71%，相较基线提升104%。消融实验表明时间建模贡献21%，PLI图构建贡献57%。额中央区域（Fz, Cz, C3, C4）为主要生物标志物，β频段贡献率达58.9%，Hjorth参数为31.2%；而Cz-T7连接在个体层面保持稳定，可作为客观筛查的特质性标志。
原文摘要 · Abstract (English)
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.
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