Detection of Mental Stress through EEG Signal in Virtual Reality Environment

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, accompan...

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Bibliographic Details
Published in:Electronics (Basel) 2021-11, Vol.10 (22), p.2840
Main Authors: Kamińska, Dorota, Smółka, Krzysztof, Zwoliński, Grzegorz
Format: Article
Language:English
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
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10222840