PDF(Pubmed)
Abstract：
This research aims to establish a practical stress detection framework by integrating physiological indicators and deep learning techniques. Utilizing a virtual reality (VR) interview paradigm mirroring real-world scenarios, our focus is on classifying stress states through accessible single-channel electroencephalogram (EEG) and galvanic skin response (GSR) data. Thirty participants underwent stress-inducing VR interviews, with biosignals recorded for deep learning models. Five convolutional neural network (CNN) architectures and one Vision Transformer model, including a multiple-column structure combining EEG and GSR features, showed heightened predictive capabilities and an enhanced area under the receiver operating characteristic curve (AUROC) in stress prediction compared to single-column models. Our experimental protocol effectively elicited stress responses, observed through fluctuations in stress visual analogue scale (VAS), EEG, and GSR metrics. In the single-column architecture, ResNet-152 excelled with a GSR AUROC of 0.944 (±0.027), while the Vision Transformer performed well in EEG, achieving peak AUROC values of 0.886 (±0.069) respectively. Notably, the multiple-column structure, based on ResNet-50, achieved the highest AUROC value of 0.954 (±0.018) in stress classification. Through VR-based simulated interviews, our study induced social stress responses, leading to significant modifications in GSR and EEG measurements. Deep learning models precisely classified stress levels, with the multiple-column strategy demonstrating superiority. Additionally, discreetly placing single-channel EEG measurements behind the ear enhances the convenience and accuracy of stress detection in everyday situations.
摘要：
本研究旨在通过整合生理指标和深度学习技术，建立一个实用的压力检测框架。利用虚拟现实（VR）采访范式反映现实世界的场景，我们的重点是通过可访问的单通道脑电图(EEG)和皮肤电反应(GSR)数据对应激状态进行分类.30名参与者接受了压力诱导的VR采访，为深度学习模型记录生物信号。五个卷积神经网络（CNN）架构和一个视觉转换模型，包括结合EEG和GSR特征的多柱结构，与单柱模型相比，在压力预测中显示出增强的预测能力和增强的接收器工作特征曲线（AUROC）下面积。我们的实验方案有效地引发了应激反应，通过压力视觉模拟量表(VAS)的波动观察，脑电图,和GSR指标。在单列架构中，ResNet-152的GSRAUROC为0.944（±0.027），虽然视觉转换器在脑电图中表现良好，AUROC峰值分别为0.886(±0.069)。值得注意的是,多柱结构，基于ResNet-50，在应激分类中达到最高AUROC值0.954(±0.018)。通过基于VR的模拟访谈,我们的研究诱导了社会应激反应，导致GSR和EEG测量的重大修改。深度学习模型对压力水平进行精确分类，多列策略显示出优越性。此外,谨慎地将单通道脑电图测量放在耳朵后面，增强了日常情况下压力检测的便利性和准确性。
