eprintid: 8956
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/89/56
datestamp: 2023-11-09 16:20:53
lastmod: 2023-11-09 16:20:53
status_changed: 2023-11-09 16:13:56
type: conference_item
metadata_visibility: show
creators_name: Al-Shargie, F.
creators_name: Tang, T.B.
creators_name: Badruddin, N.
creators_name: Dass, S.C.
creators_name: Kiguchi, M.
title: Mental stress assessment based on feature level fusion of fNIRS and EEG signals
ispublished: pub
keywords: Feedback; Infrared devices; Near infrared spectroscopy; Support vector machines, Detection rates; Electro-encephalogram (EEG); Feature level fusion; Functional near-infrared spectroscopy (fnirs); Individual performance; Prefrontal cortex; Simultaneous measurement; Stress condition, Electroencephalography
note: cited By 12; Conference of 6th International Conference on Intelligent and Advanced Systems, ICIAS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125970
abstract: 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.
date: 2017
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011982455&doi=10.1109%2fICIAS.2016.7824060&partnerID=40&md5=df408c0a5a61773b085d43e4c4ae25e7
id_number: 10.1109/ICIAS.2016.7824060
full_text_status: none
publication: International Conference on Intelligent and Advanced Systems, ICIAS 2016
refereed: TRUE
isbn: 9781509008452
citation:   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.  In: UNSPECIFIED.