Mental stress assessment based on feature level fusion of fNIRS and EEG signals

This study aims to improve the detection rate of mental stress using the complementary nature of functional Near Infrared Spectroscopy (fNIRS) and Electroencephalogram (EEG). Simultaneous measurements of fNIRS and EEG signals were conducted on 12 subjects while solving arithmetic problems under two...

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Main Authors: Al-Shargie, F., Tang, T.B., Badruddin, N., Dass, S.C., Kiguchi, M.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.20226 /
Published: Institute of Electrical and Electronics Engineers Inc. 2017
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011982455&doi=10.1109%2fICIAS.2016.7824060&partnerID=40&md5=df408c0a5a61773b085d43e4c4ae25e7
http://eprints.utp.edu.my/20226/
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spelling utp-eprints.202262018-04-22T14:46:24Z Mental stress assessment based on feature level fusion of fNIRS and EEG signals Al-Shargie, F. Tang, T.B. Badruddin, N. Dass, S.C. Kiguchi, M. This study aims to improve the detection rate of mental stress using the complementary nature of functional Near Infrared Spectroscopy (fNIRS) and Electroencephalogram (EEG). Simultaneous measurements of fNIRS and EEG signals were conducted on 12 subjects while solving arithmetic problems under two different conditions (control and stress). The stressors in this work were time pressure and negative feedback of individual performance. The study demonstrated significant reduction in the concentration of oxygenated haemoglobin (p=0.0032) and alpha rhythm power (p=0.0213) on the prefrontal cortex (PFC) under stress condition. Specifically, the right PFC and dorsolateral PFC were highly sensitive to mental stress. Using support vector machine (SVM), the mean detection rate of mental stress was 91, 95 and 98 using fNIRS, EEG and fusion of fNIRS and EEG signals, respectively. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011982455&doi=10.1109%2fICIAS.2016.7824060&partnerID=40&md5=df408c0a5a61773b085d43e4c4ae25e7 Al-Shargie, F. and Tang, T.B. and Badruddin, N. and Dass, S.C. and Kiguchi, M. (2017) Mental stress assessment based on feature level fusion of fNIRS and EEG signals. International Conference on Intelligent and Advanced Systems, ICIAS 2016 . http://eprints.utp.edu.my/20226/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description This study aims to improve the detection rate of mental stress using the complementary nature of functional Near Infrared Spectroscopy (fNIRS) and Electroencephalogram (EEG). Simultaneous measurements of fNIRS and EEG signals were conducted on 12 subjects while solving arithmetic problems under two different conditions (control and stress). The stressors in this work were time pressure and negative feedback of individual performance. The study demonstrated significant reduction in the concentration of oxygenated haemoglobin (p=0.0032) and alpha rhythm power (p=0.0213) on the prefrontal cortex (PFC) under stress condition. Specifically, the right PFC and dorsolateral PFC were highly sensitive to mental stress. Using support vector machine (SVM), the mean detection rate of mental stress was 91, 95 and 98 using fNIRS, EEG and fusion of fNIRS and EEG signals, respectively. © 2016 IEEE.
format Article
author Al-Shargie, F.
Tang, T.B.
Badruddin, N.
Dass, S.C.
Kiguchi, M.
spellingShingle Al-Shargie, F.
Tang, T.B.
Badruddin, N.
Dass, S.C.
Kiguchi, M.
Mental stress assessment based on feature level fusion of fNIRS and EEG signals
author_sort Al-Shargie, F.
title Mental stress assessment based on feature level fusion of fNIRS and EEG signals
title_short Mental stress assessment based on feature level fusion of fNIRS and EEG signals
title_full Mental stress assessment based on feature level fusion of fNIRS and EEG signals
title_fullStr Mental stress assessment based on feature level fusion of fNIRS and EEG signals
title_full_unstemmed Mental stress assessment based on feature level fusion of fNIRS and EEG signals
title_sort mental stress assessment based on feature level fusion of fnirs and eeg signals
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011982455&doi=10.1109%2fICIAS.2016.7824060&partnerID=40&md5=df408c0a5a61773b085d43e4c4ae25e7
http://eprints.utp.edu.my/20226/
_version_ 1741196337951539200
score 11.62408

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