Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted to detect mental stress based on biological and biochemical effects. Moreover, hormones, physiological effects, and skin temperature have been extensively used for stress detection. However, based on the recent literature, biological, biochemical, and physiological-based methods have shown inconsistent findings, which are initiated due to hormones’ instability. Therefore, it is crucial to study stress using different mechanisms such as Electroencephalogram (EEG) signals. In this research study, the frontal lobes EEG spectrum analysis is applied to detect mental stress. Initially, we apply a Fast Fourier Transform (FFT) as a feature extraction stage to measure all bands’ power density for the frontal lobe. After that, we used two type of classifications such as subject wise and mix (mental stress vs. control) using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning classifiers. Our obtained results of the average subject wise classification showed that the proposed technique has better accuracy (98.21%). Moreover, this technique has low complexity, high accuracy, simple and easy to use, no over fitting, and it could be used as a real-time and continuous monitoring technique for medical applications.
Cite this article
Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection
Omar AlShorman1, Mahmoud Masadeh2,*, Md Belal Bin Heyat3,4,5,*, Faijan Akhtar6, Hossam Almahasneh7, Ghulam Md Ashraf8,9, Athanasios Alexiou5,10,*
1 College of Engineering, Najran University, 55461 Najran, Saudi Arabia
2 Computer Engineering Department, Yarmouk University, 21163 Irbid, Jordan
3 IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, 518060 Shenzhen, Guangdong, China
4 School of Electronic Science and Engineering, University of Electronic Science and Technology of China, 610054 Chengdu, Sichuan, China
5 Novel Global Community Educational Foundation, NSW 2770 Hebersham, Australia
6 School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, Sichuan, China
7 AI and ML specialist, Dubai Taxi Corporation, 2647 Dubai, UAE
8 Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, 21589 Jeddah, Saudi Arabia
9 Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, 21589 Jeddah, Saudi Arabia
10 AFNP Med Austria, 2770 Wien, Austria
*Correspondence: email@example.com (Mahmoud Masadeh); firstname.lastname@example.org (Md Belal Bin Heyat); email@example.com (Athanasios Alexiou)
J. Integr. Neurosci. 2022, 21(1), 20; https://doi.org/10.31083/j.jin2101020
Submitted: 23 February 2021 | Revised: 9 March 2021 | Accepted: 6 April 2021 | Published: 28 January 2022
(This article belongs to the Special Issue Biomedical informatics in neuroscience)
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
Fast fourier transform