Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm.

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-co

SUBMITTER: Kim HG 

PROVIDER: S-EPMC11221693 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature