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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Autores principales:	Dorota Kamińska, Krzysztof Smółka, Grzegorz Zwoliński
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	virtual reality (VR) mental stress detection electroencephalography (EEG) eye movement desensitization and reprocessing (EMDR) affective computing machine learning Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/a74d237223ee4e489586be84976c8423
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id	oai:doaj.org-article:a74d237223ee4e489586be84976c8423
record_format	dspace
spelling	oai:doaj.org-article:a74d237223ee4e489586be84976c84232021-11-25T17:25:12ZDetection of Mental Stress through EEG Signal in Virtual Reality Environment10.3390/electronics102228402079-9292https://doaj.org/article/a74d237223ee4e489586be84976c84232021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2840https://doaj.org/toc/2079-9292This 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.Dorota KamińskaKrzysztof SmółkaGrzegorz ZwolińskiMDPI AGarticlevirtual reality (VR)mental stress detectionelectroencephalography (EEG)eye movement desensitization and reprocessing (EMDR)affective computingmachine learningElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2840, p 2840 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	virtual reality (VR) mental stress detection electroencephalography (EEG) eye movement desensitization and reprocessing (EMDR) affective computing machine learning Electronics TK7800-8360
spellingShingle	virtual reality (VR) mental stress detection electroencephalography (EEG) eye movement desensitization and reprocessing (EMDR) affective computing machine learning Electronics TK7800-8360 Dorota Kamińska Krzysztof Smółka Grzegorz Zwoliński Detection of Mental Stress through EEG Signal in Virtual Reality Environment
description	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.
format	article
author	Dorota Kamińska Krzysztof Smółka Grzegorz Zwoliński
author_facet	Dorota Kamińska Krzysztof Smółka Grzegorz Zwoliński
author_sort	Dorota Kamińska
title	Detection of Mental Stress through EEG Signal in Virtual Reality Environment
title_short	Detection of Mental Stress through EEG Signal in Virtual Reality Environment
title_full	Detection of Mental Stress through EEG Signal in Virtual Reality Environment
title_fullStr	Detection of Mental Stress through EEG Signal in Virtual Reality Environment
title_full_unstemmed	Detection of Mental Stress through EEG Signal in Virtual Reality Environment
title_sort	detection of mental stress through eeg signal in virtual reality environment
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a74d237223ee4e489586be84976c8423
work_keys_str_mv	AT dorotakaminska detectionofmentalstressthrougheegsignalinvirtualrealityenvironment AT krzysztofsmołka detectionofmentalstressthrougheegsignalinvirtualrealityenvironment AT grzegorzzwolinski detectionofmentalstressthrougheegsignalinvirtualrealityenvironment
_version_	1718412367453224960

Detection of Mental Stress through EEG Signal in Virtual Reality Environment

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