Detection of Mental Stress through EEG Signal in Virtual Reality Environment

• Published in: Electronics (Basel) Vol. 10; no. 22; p. 2840
• Main Authors: Kamińska, Dorota, Smółka, Krzysztof, Zwoliński, Grzegorz
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2021
• Subjects: Accuracy; Algorithms; Anxiety; Artificial neural networks; Classification; Computer simulation; Discriminant analysis; Electroencephalography; Emotions; Investigations; Machine learning; Multilayer perceptrons; Nervous system; Psychological stress; Psychotherapy; Signal classification; Signal monitoring; Stress; Support vector machines; Traffic congestion; Virtual reality
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics10222840

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.