What is the clinical evidence from this study?

Study design: Observational. Population: Mental stress (n=28). Intervention: EEG signal classification during virtual reality. Primary outcome: Classification accuracy of stress level.

November 18, 2021Electronics76 citationsOpen Access

Detection of Mental Stress through EEG Signal in Virtual Reality Environment

Q: What does this research mean for the field?

EEG signals can be used to classify mental stress levels during virtual reality sessions with up to 96.42% accuracy using multilayer perceptron and Support Vector Machine classifiers. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

DKDorota KamińskaLodz University of Technology KSKrzysztof SmółkaInstitute of Information Science GZGrzegorz ZwolińskiLodz University of Technology

Key Result

EEG signal classification of mental stress levels during a virtual reality session achieved an accuracy of 96.42% using multilayer perceptron and Support Vector Machine classifiers.

Study Design

Type

Observational (n=28)

Structured PICO

Can EEG signals accurately classify mental stress levels during a virtual reality session in healthy adults?

Population

28 healthy adult volunteers (office workers)

Intervention

EEG signal monitoring using EMOTIV EPOC Flex wireless head cap system during a VR session with alternating relaxing and stressful scenes

Outcome

Classification of stress levelsurrogate

EEG signals can accurately classify mental stress levels in a virtual reality environment using machine learning algorithms.

Abstract

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.

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