Development of neurometric acute stress assessment based on EEG signals

Nowadays, stress is one of the major issues where too much stress may lead to depression, fatigue and insomnia. Stress can be divided into two types called Eustress and Distress. Eustress or positive stress refers to the positive stress which helps to improve the performance of an individual. In c...

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Main Author:	Saidatul Ardeenawatie, Awang
Format:	Thesis
Language:	English
Subjects:	Stress Neurometric acute Electroencephalography (EEG) Eustress Distress
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44127/1/p.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44127/2/Full%20text.pdf
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spelling	my-unimap-441272016-11-22T08:03:36Z Development of neurometric acute stress assessment based on EEG signals Saidatul Ardeenawatie, Awang Nowadays, stress is one of the major issues where too much stress may lead to depression, fatigue and insomnia. Stress can be divided into two types called Eustress and Distress. Eustress or positive stress refers to the positive stress which helps to improve the performance of an individual. In contrast, Distress or negative stress can devastate a person by creating depression and damage the quality of life. It is essential to comprehend and to figure out the state of current stress in numerical index. The development of a reliable data acquisition protocol is a crucial part to elicit mental stress in different level of stress. In this study, some modification on the existing Mental Arithmetic Task (MAT) has been made to ensure the designed protocol is capable to induce the different intensity of stress such as low, moderate and high. The dynamical excitation protocol and time pressure concept are proposed in this work. There are three validation methods have been used, namely, K Nearest Neighbor (KNN), Alpha Brain Asymmetry and statistical analysis (Paired T-test). As a result of this study, it was found that the proposed experimental protocol is comparable as the verification has been made with the following: (i) The t-test result based on physiological changes during pre and post experiment were found to be statistically significant (p<0.01) (ii) The mean value of Alpha Brain Asymmetry are comparable and have a potential to discriminate between levels and (iii) the classification accuracy of 84% confirmed that the proposed protocol have potential in classifying the mental stress level. Besides that, the preprocessing technique applying elliptic filters with 256 data per frame is the most suitable technique. Five types of spectral estimator (Welch, Burg, Yule Walker, Modified Covariance and Multiple Signal Classification) based feature extraction is performed on the normalized signals. The extracted features are cross validated using 10-fold cross validation and classified using KNN and have been proved using statistical analysis (ANOVA). The maximum mean classification rate of 86.75% is achieved using Modified Covariance feature derived from alpha waves using KNN. Besides that, this study found that F3 and F4 are the most informative electrodes with the classification rate of 93.50%. Last but not least, a new algorithm has been proposed based on the more established index, Alpha Asymmetry Score (AAS), as a reference. Modifications have been made in term of the frequency band as a variable in the stress index. The classification accuracy of the proposed Stress Asymmetry Score (SAS) is approximately 96% which is 10% higher than AAS. The development of the stress index promises new era of stress brain related research for future people’s benefit. Universiti Malaysia Perlis (UniMAP) 2014 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/44127 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44127/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44127/1/p.1-24.pdf f9e67a1854bdb2198d9cf238d8fd6e70 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44127/2/Full%20text.pdf 3cf2950df902c29a6a239752ea4b8614 Stress Neurometric acute Electroencephalography (EEG) Eustress Distress School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Stress Neurometric acute Electroencephalography (EEG) Eustress Distress
spellingShingle	Stress Neurometric acute Electroencephalography (EEG) Eustress Distress Saidatul Ardeenawatie, Awang Development of neurometric acute stress assessment based on EEG signals
description	Nowadays, stress is one of the major issues where too much stress may lead to depression, fatigue and insomnia. Stress can be divided into two types called Eustress and Distress. Eustress or positive stress refers to the positive stress which helps to improve the performance of an individual. In contrast, Distress or negative stress can devastate a person by creating depression and damage the quality of life. It is essential to comprehend and to figure out the state of current stress in numerical index. The development of a reliable data acquisition protocol is a crucial part to elicit mental stress in different level of stress. In this study, some modification on the existing Mental Arithmetic Task (MAT) has been made to ensure the designed protocol is capable to induce the different intensity of stress such as low, moderate and high. The dynamical excitation protocol and time pressure concept are proposed in this work. There are three validation methods have been used, namely, K Nearest Neighbor (KNN), Alpha Brain Asymmetry and statistical analysis (Paired T-test). As a result of this study, it was found that the proposed experimental protocol is comparable as the verification has been made with the following: (i) The t-test result based on physiological changes during pre and post experiment were found to be statistically significant (p<0.01) (ii) The mean value of Alpha Brain Asymmetry are comparable and have a potential to discriminate between levels and (iii) the classification accuracy of 84% confirmed that the proposed protocol have potential in classifying the mental stress level. Besides that, the preprocessing technique applying elliptic filters with 256 data per frame is the most suitable technique. Five types of spectral estimator (Welch, Burg, Yule Walker, Modified Covariance and Multiple Signal Classification) based feature extraction is performed on the normalized signals. The extracted features are cross validated using 10-fold cross validation and classified using KNN and have been proved using statistical analysis (ANOVA). The maximum mean classification rate of 86.75% is achieved using Modified Covariance feature derived from alpha waves using KNN. Besides that, this study found that F3 and F4 are the most informative electrodes with the classification rate of 93.50%. Last but not least, a new algorithm has been proposed based on the more established index, Alpha Asymmetry Score (AAS), as a reference. Modifications have been made in term of the frequency band as a variable in the stress index. The classification accuracy of the proposed Stress Asymmetry Score (SAS) is approximately 96% which is 10% higher than AAS. The development of the stress index promises new era of stress brain related research for future people’s benefit.
format	Thesis
author	Saidatul Ardeenawatie, Awang
author_facet	Saidatul Ardeenawatie, Awang
author_sort	Saidatul Ardeenawatie, Awang
title	Development of neurometric acute stress assessment based on EEG signals
title_short	Development of neurometric acute stress assessment based on EEG signals
title_full	Development of neurometric acute stress assessment based on EEG signals
title_fullStr	Development of neurometric acute stress assessment based on EEG signals
title_full_unstemmed	Development of neurometric acute stress assessment based on EEG signals
title_sort	development of neurometric acute stress assessment based on eeg signals
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44127/1/p.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44127/2/Full%20text.pdf
_version_	1747836825713508352

Development of neurometric acute stress assessment based on EEG signals

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