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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/21406

Title: Development of EEG-based stress index
Authors: Norizam, Sulaiman
Mohd Nasir, Taib, Prof. Dr.
Sahrim, Lias
Zunairah, Hj Murat
Siti Armiza, Mohd Aris
Mahfuza, Mustafa
Nazre, Abdul Rashid
???metadata.dc.contributor.url???: norizam@ump.edu.my
Keywords: Cognitive states;Electroencephalogram (EEG);Energy Spectral Density;Shannon Entropy;Spectral Centroids;k-Nearest Neighbor (k-NN);Stress Index
Issue Date: 27-Feb-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: p. 461-466
Series/Report no.: Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012)
Abstract: 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%.
Description: Link to publisher's homepage at http://ieeexplore.ieee.org/
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6179059
ISBN: 978-145771989-9
Appears in Collections:Conference Papers

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