一种基于ECG时频谱和fNIRS前额叶皮层网络的CNN-GAT融合模型：用于评估热应激下核电厂操作员绩效的神经工效学模型
A neuroergonomics model to evaluating nuclear power plants operators' performance under heat stress driven by ECG time-frequency spectrums and fNIRS prefrontal cortex network: a CNN-GAT fusion model
AI总结：
本文提出一种融合CNN与GAT的神经工效学模型，利用ECG时频谱和fNIRS前额叶皮层网络评估热应激下核电厂操作员绩效，达到0.90 AUC，为工业5.0关键人机系统提供人体状态评估方法。
摘要：
操作员在暴露于极端热应激时会出现复杂的生理和心理状态，这会损害认知功能并显著降低绩效，最终导致严重的次生灾害。因此，迫切需要一种可行的技术来识别其异常状态，以提高人-控制论系统的可靠性。随着深度学习在生理建模中的进展，提出了一种由心电图（ECG）和功能性近红外光谱（fNIRS）驱动的操作员绩效评估模型，展示了较高的生态效度。该模型融合了卷积神经网络（CNN）主干和圖注意力网络（GAT）主干，分别从ECG时频谱和fNIRS前额叶皮层（PFC）网络中提取判别性特征，并融入了更深的神经科学领域知识，最终达到了0.90的AUC。结果支持了由专门神经科学方法提取的手工特征可以缓解过拟合。受脑网络小世界特性的启发，fNIRS PFC网络被组织为无向图并由GAT嵌入。事实证明，与简单的非线性变换相比，它在信息聚合和传递方面表现更好。该模型为工业5.0场景下关键人-控制论系统中的人体状态评估提供了一种潜在的神经工效学应用。
英文摘要：
Operators experience complicated physiological and psychological states when exposed to extreme heat stress, which can impair cognitive function and decrease performance significantly, ultimately leading to severe secondary disasters. Therefore, there is an urgent need for a feasible technique to identify their abnormal states to enhance the reliability of human-cybernetics systems. With the advancement of deep learning in physiological modeling, a model for evaluating operators' performance driven by electrocardiogram (ECG) and functional near-infrared spectroscopy (fNIRS) was proposed, demonstrating high ecological validity. The model fused a convolutional neural network (CNN) backbone and a graph attention network (GAT) backbone to extract discriminative features from ECG time-frequency spectrums and fNIRS prefrontal cortex (PFC) network respectively with deeper neuroscience domain knowledge, and eventually achieved 0.90 AUC. Results supported that handcrafted features extracted by specialized neuroscience methods can alleviate overfitting. Inspired by the small-world nature of the brain network, the fNIRS PFC network was organized as an undirected graph and embedded by GAT. It is proven to perform better in information aggregation and delivery compared to a simple non-linear transformation. The model provides a potential neuroergonomics application for evaluating the human state in vital human-cybernetics systems under industry 5.0 scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
