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Research Article | Open Access

Electroencephalogram Based Stress Detection Using Extreme Learning Machine

Mousa K. Wali( ), Rashid Ali Fayadh, Nabil K. Al_shamaa
College of Electrical Engineering, Middle Technical University, Baghdad, Iraq
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Abstract

The detection of stress is important because it contributes to diverse pathophysiological changes including sudden death. Various techniques have been used to evaluate stress in terms of questionnaire or by quantifying the changes of physiological signals. Electroencephalogram signals are highly useful in measuring human stress. Therefore, to solve and detect stress problem, this work had extracted electroencephalogram features of theta, alpha, and beta bands in the frequency domain by wavelet packet transform because these bands are concerned with stress. In this research four features have been supplied to extreme learning machine which gave accuracy of 98.56% of detecting stress from normal state based on db4 with an average sensitivity of 92.52% and specificity of 95.88%. This research studied the stress on 15 students due to mathematical exercises in a noisy environment with different stimulus.

Keywords

Extreme learning machineElectroencephalogramWavelet packet transformKurtosis

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Nano Biomedicine and Engineering
Volume 14 Issue 3,
September 2022
Pages 208-215
DOI: 10.5101/nbe.v14i3.p208-215
Cite this article:
Wali MK, Fayadh RA, Al_shamaa NK. Electroencephalogram Based Stress Detection Using Extreme Learning Machine. Nano Biomedicine and Engineering, 2022, 14(3): 208-215. https://doi.org/10.5101/nbe.v14i3.p208-215

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Received: 30 April 2022
Revised: 26 October 2022
Accepted: 14 November 2022
Published: 30 November 2022
© Mousa K. Wali, Rashid Ali Fayadh and Nabil K. Al_shamaa.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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