NeuroStrata：用于精神压力动态脑网络分析的脑电连接感知深度表示学习框架
NeuroStrata: An Electroencephalographic Connectivity-Aware Deep Representation Learning Framework for Dynamic Brain Network Analysis of Mental Stress
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University（迪肯大学智能系统研究与创新研究所）
- Centre for Health Research, University of Southern Queensland（南昆士兰大学健康研究中心）
- School of Science, Engineering and Digital Technologies, University of Southern Queensland（南昆士兰大学科学、工程与数字技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出NeuroStrata框架，结合时变有效连接建模与深度表示学习，利用EEG信号分析精神压力，β波段连接准确率达97.3%，为该领域提供可解释自动化方法。
AI中文摘要：
本研究提出NeuroStrata，一种基于脑电（EEG）的精神压力分析的连接感知深度表示学习框架，采用时变偏定向相干性（TV-PDC）技术。与基于静态特征的传统EEG分类方法不同，NeuroStrata对分布脑区间特定频率定向连接的时间演化进行建模。使用32通道SAM 40数据集在心算任务期间记录的EEG信号生成TV-PDC连接图，这些图通过预训练的卷积神经网络（CNN）和视觉Transformer（ViT）处理以提取深度连接嵌入，随后使用轻量级机器学习模型进行分类。实验结果表明，β波段连接具有最高的判别能力，采用LAION-CLIP-ViT-L14主干网络结合支持向量机（SVM）分类器时，峰值准确率达97.3%；而α波段连接在各模型配置下均表现出稳定性能。连接分析揭示了与压力相关神经动态相关的显著额叶驱动α影响及中枢整合β连接模式。时间评估进一步表明，分类性能在中晚期时间窗口趋于稳定，提示压力相关连接特征逐步整合。该框架将时变有效连接建模与深度表示学习相结合，为基于EEG的精神压力分析提供了可解释且自动化的方法。
英文摘要：
This study introduces NeuroStrata, a connectivity-aware deep representation learning framework for EEG-based mental stress analysis using Time-Varying Partial Directed Coherence (TV-PDC). Unlike conventional EEG classification approaches based on static features, NeuroStrata models the temporal evolution of frequency-specific directed connectivity across distributed brain regions. EEG signals from the 32-channel SAM 40 dataset recorded during mental arithmetic tasks were used to generate TV-PDC connectivity maps. These maps were processed using pretrained Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to extract deep connectivity embeddings, which were subsequently classified using lightweight machine learning models. Experimental results demonstrate that beta-band connectivity provides the highest discriminative capability, achieving a peak accuracy of 97.3% using the LAION-CLIP-ViT-L14 backbone with a Support Vector Machine classifier, while alpha-band connectivity exhibits consistently stable performance across model configurations. Connectivity analysis revealed prominent frontal-driven alpha influences and centrally integrated beta connectivity patterns associated with stress-related neural dynamics. Temporal evaluation further indicated that classification performance stabilizes in mid-to-late temporal windows, suggesting progressive consolidation of stress-related connectivity signatures. The proposed framework integrates time-varying effective connectivity modelling with deep representation learning to provide an interpretable and automated approach for EEG-based mental stress analysis.
