Development of EEG-based stress index

This paper presents a non-parametric method to produce stress index using Electroencephalogram (EEG) signals. 180 EEG datasets from healthy subjects were evaluated at two cognitive states; resting state (Eyes Closed) and working state (Eyes Open). In working cognitive state, subjects were asked to a...

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Main Authors:	Norizam, Sulaiman, Mohd Nasir, Taib, Sahrim, Lias, Zunairah, Murat, Siti Armiza, Mohd Aris, Mahfuzah, Mustafa, Nazre, Abdul Rashid
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2012
Subjects:	QC Physics TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/25430/ http://umpir.ump.edu.my/id/eprint/25430/ http://umpir.ump.edu.my/id/eprint/25430/1/Development%20of%20EEG-based%20stress%20index.pdf

id	ump-25430
recordtype	eprints
spelling	ump-254302019-11-13T02:00:10Z http://umpir.ump.edu.my/id/eprint/25430/ Development of EEG-based stress index Norizam, Sulaiman Mohd Nasir, Taib Sahrim, Lias Zunairah, Murat Siti Armiza, Mohd Aris Mahfuzah, Mustafa Nazre, Abdul Rashid QC Physics TK Electrical engineering. Electronics Nuclear engineering This paper presents a non-parametric method to produce stress index using Electroencephalogram (EEG) signals. 180 EEG datasets from healthy subjects were evaluated at two cognitive states; resting state (Eyes Closed) and working state (Eyes Open). In working cognitive state, subjects were asked to answer the Intelligence Quotient (IQ) test questions. The EEG datasets were categorized into 4 groups. Energy Spectral Density (ESD) ratios and Spectral Centroids (SC) from the two tasks were calculated and selected as input features to k-Nearest Neighbor (k-NN) classifier. Shannon's Entropy (SE) was used to detect and quantify the distribution of ESD due to stressors (stress factors). The stress indexes were assigned based on the results of classification, ESD ratios, SC and SE. There were 3 types of stress indexes can be assigned which represent the stress level (low stress, moderate stress and high stress) at classification accuracy of 88.89%. The regression coefficient of the SC of Beta and Alpha was 77%. IEEE 2012-04-05 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25430/1/Development%20of%20EEG-based%20stress%20index.pdf Norizam, Sulaiman and Mohd Nasir, Taib and Sahrim, Lias and Zunairah, Murat and Siti Armiza, Mohd Aris and Mahfuzah, Mustafa and Nazre, Abdul Rashid (2012) Development of EEG-based stress index. In: International Conference on Biomedical Engineering, ICoBE 2012, 27-28 Feb. 2012 , Penang, Malaysia. pp. 461-466. (6179059). ISBN 978-1-4577-1990-5 (Print); 978-1-4577-1991-2 (Online); 978-1-4577-1989-9 (CD ROM) https://doi.org/10.1109/ICoBE.2012.6179059
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QC Physics TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QC Physics TK Electrical engineering. Electronics Nuclear engineering Norizam, Sulaiman Mohd Nasir, Taib Sahrim, Lias Zunairah, Murat Siti Armiza, Mohd Aris Mahfuzah, Mustafa Nazre, Abdul Rashid Development of EEG-based stress index
description	This paper presents a non-parametric method to produce stress index using Electroencephalogram (EEG) signals. 180 EEG datasets from healthy subjects were evaluated at two cognitive states; resting state (Eyes Closed) and working state (Eyes Open). In working cognitive state, subjects were asked to answer the Intelligence Quotient (IQ) test questions. The EEG datasets were categorized into 4 groups. Energy Spectral Density (ESD) ratios and Spectral Centroids (SC) from the two tasks were calculated and selected as input features to k-Nearest Neighbor (k-NN) classifier. Shannon's Entropy (SE) was used to detect and quantify the distribution of ESD due to stressors (stress factors). The stress indexes were assigned based on the results of classification, ESD ratios, SC and SE. There were 3 types of stress indexes can be assigned which represent the stress level (low stress, moderate stress and high stress) at classification accuracy of 88.89%. The regression coefficient of the SC of Beta and Alpha was 77%.
format	Conference or Workshop Item
author	Norizam, Sulaiman Mohd Nasir, Taib Sahrim, Lias Zunairah, Murat Siti Armiza, Mohd Aris Mahfuzah, Mustafa Nazre, Abdul Rashid
author_facet	Norizam, Sulaiman Mohd Nasir, Taib Sahrim, Lias Zunairah, Murat Siti Armiza, Mohd Aris Mahfuzah, Mustafa Nazre, Abdul Rashid
author_sort	Norizam, Sulaiman
title	Development of EEG-based stress index
title_short	Development of EEG-based stress index
title_full	Development of EEG-based stress index
title_fullStr	Development of EEG-based stress index
title_full_unstemmed	Development of EEG-based stress index
title_sort	development of eeg-based stress index
publisher	IEEE
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/25430/ http://umpir.ump.edu.my/id/eprint/25430/ http://umpir.ump.edu.my/id/eprint/25430/1/Development%20of%20EEG-based%20stress%20index.pdf
first_indexed	2023-09-18T22:39:02Z
last_indexed	2023-09-18T22:39:02Z
_version_	1777416790877405184

Development of EEG-based stress index

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