eprintid: 10941
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/09/41
datestamp: 2023-11-09 16:37:33
lastmod: 2023-11-09 16:37:33
status_changed: 2023-11-09 16:32:33
type: article
metadata_visibility: show
creators_name: Al-shargie, F.
creators_name: Tang, T.B.
creators_name: Badruddin, N.
creators_name: Kiguchi, M.
title: Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach
ispublished: pub
keywords: Discriminant analysis; Electrophysiology; Feedback; NASA; Neuroimaging; Stresses; Support vector machines, Assessment protocols; Classification accuracy; Clinical interventions; Contributing factor; Discriminant analysis methods; Error correcting output code; Multiclass support vector machines; SVM+ECOC, Electroencephalography, accuracy; adult; alpha rhythm; Article; classifier; controlled study; discriminant analysis; electroencephalography; error correcting output code; feasibility study; human; human experiment; male; mental arithmetic; mental stress; multilevel analysis; negative feedback; normal human; prefrontal cortex; priority journal; questionnaire; self report; spectrometry; stress assessment; support vector machine; young adult
note: cited By 108
abstract: Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p values < 0.001. Furthermore, we found a significant reduction in alpha rhythm power from one stress level to another level, p values < 0.05. In comparison, results from self-reporting questionnaire NASA-TLX approach showed no significant differences between stress levels. In addition, we developed a discriminant analysis method based on multiclass support vector machine (SVM) with error-correcting output code (ECOC). Different stress levels were detected with an average classification accuracy of 94.79. The lateral index (LI) results further showed dominant right prefrontal cortex (PFC) to mental stress (reduced alpha rhythm). The study demonstrated the feasibility of using EEG in classifying multilevel mental stress and reported alpha rhythm power at right prefrontal cortex as a suitable index. © 2017, International Federation for Medical and Biological Engineering.
date: 2018
publisher: Springer Verlag
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031494978&doi=10.1007%2fs11517-017-1733-8&partnerID=40&md5=310b006303f5e6a26a5a94123bb01f1f
id_number: 10.1007/s11517-017-1733-8
full_text_status: none
publication: Medical and Biological Engineering and Computing
volume: 56
number: 1
pagerange: 125-136
refereed: TRUE
issn: 01400118
citation:   Al-shargie, F. and Tang, T.B. and Badruddin, N. and Kiguchi, M.  (2018) Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach.  Medical and Biological Engineering and Computing, 56 (1).  pp. 125-136.  ISSN 01400118