Detection of Mental Stress through EEG Signal in Virtual Reality Environment

Electronics

Krzysztof Smółka¹  Dorota Kamińska¹  Grzegorz Zwoliński¹ 

[1] Institute of Mechatronics and Information Systems, Lodz University of Technology, Stefanowskiego 22 Str., 90-924 Lodz, Poland;

关键词: virtual reality (VR);    mental stress detection;    electroencephalography (EEG);    eye movement desensitization and reprocessing (EMDR);    affective computing;    machine learning;   

DOI  :  10.3390/electronics10222840

来源: DOAJ

【 摘 要 】

This paper investigates the use of an electroencephalogram (EEG) signal to classify a subject’s stress level while using virtual reality (VR). For this purpose, we designed an acquisition protocol based on alternating relaxing and stressful scenes in the form of a VR interactive simulation, accompanied by an EEG headset to monitor the subject’s psycho-physical condition. Relaxation scenes were developed based on scenarios created for psychotherapy treatment utilizing bilateral stimulation, while the Stroop test worked as a stressor. The experiment was conducted on a group of 28 healthy adult volunteers (office workers), participating in a VR session. Subjects’ EEG signal was continuously monitored using the EMOTIV EPOC Flex wireless EEG head cap system. After the session, volunteers were asked to re-fill questionnaires regarding the current stress level and mood. Then, we classified the stress level using a convolutional neural network (CNN) and compared the classification performance with conventional machine learning algorithms. The best results were obtained considering all brain waves (96.42%) with a multilayer perceptron (MLP) and Support Vector Machine (SVM) classifiers.

【 授权许可】

Unknown