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

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 .

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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.

Item Type:Article
Impact Factor:cited By 0
Departments / MOR / COE:Centre of Excellence > Center for Intelligent Signal and Imaging Research
ID Code:20226
Deposited By: Ahmad Suhairi
Deposited On:22 Apr 2018 14:46
Last Modified:22 Apr 2018 14:46

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