TY  - CONF
N2  - Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack and stroke. To avoid this, stress quantification is very important for clinical intervention and disease prevention. In this study, we investigate the feasibility of exploiting Electroencephalography (EEG) signals to discriminate stress from rest state in mental arithmetic tasks. The experimental results showed that there were significant differences between the rest state and under stress at three levels of arithmetic task levels with p-values of 0.03, 0.042 and 0.05, respectively. We thus confirm the feasibility of EEG signals in detecting mental stress levels. Using support vector machine (SVM) we could detect mental stress with an accuracy of 94, 85, and 80 at level one, level two and level three of arithmetic problem difficulty respectively. © International Federation for Medical and Biological Engineering 2016.
N1  - cited By 41; Conference of International Conference for Innovation in Biomedical Engineering and Life Sciences, ICIBEL 2015 ; Conference Date: 6 December 2015 Through 8 December 2015; Conference Code:158329
KW  - Biomedical engineering; Electrophysiology; Signal detection; Stresses; Support vector machines; Wavelet transforms
KW  -  Arithmetic tasks; Clinical interventions; Contributing factor; Disease prevention; Mental arithmetic; Mental stress; Problem difficulty; SVM
KW  -  Electroencephalography
TI  - Mental stress quantification using EEG signals
ID  - scholars8007
SP  - 15
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952837454&doi=10.1007%2f978-981-10-0266-3_4&partnerID=40&md5=1b13a62dce26a5be1692a9d9f578d77a
A1  - Al-Shargie, F.M.
A1  - Tang, T.B.
A1  - Badruddin, N.
A1  - Kiguchi, M.
VL  - 56
EP  - 19
Y1  - 2016///
SN  - 16800737
PB  - Springer Verlag
ER  -