NDLI: Development of EEG-based stress index

Content Provider	IEEE Xplore Digital Library
Author	Sulaiman, N. Taib, M.N. Lias, S. Murat, Z.H. Aris, S.A.M. Mustafa, M. Rashid, N.A.
Copyright Year	2012
Description	Author affiliation: Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, 26300 Kuantan, Malaysia (Sulaiman, N.) \|\| Faculty of Electrical Engineering, Universiti Tecknologi MARA, 40450 Shah Alam, Selangor, Malaysia (Taib, M.N.; Lias, S.; Murat, Z.H.; Aris, S.A.M.; Mustafa, M.; Rashid, N.A.)
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%.
Starting Page	461
Ending Page	466
File Size	962158
Page Count	6
File Format	PDF
ISBN	9781457719905
e-ISBN	9781457719912
DOI	10.1109/ICoBE.2012.6179059
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-02-27
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	K-NN Energy Spectral Density Shannon Entropy Cognitive states Humans EEG Electrostatic discharges Electroencephalography Entropy Indexes Stress Stress Index Spectral Centroids Physiology
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

EEG-based Stress Features Using Spectral Centroids Technique and k-Nearest Neighbor Classifier

Stress features identification from EEG signals using EEG Asymmetry & Spectral Centroids techniques

Emotional identification during mobile RF Radiation

Development of Brainwave Balancing Index Using EEG

Classification of frontal alpha asymmetry using k-Nearest Neighbor

Brainwave sub-band power ratio characteristics in intelligence assessment

Brainwaves stress pattern based on perceived stress scale test

Brainwave Classification for Acute Ischemic Stroke Group Level Using k-NN Technique

Presenting an effective EEG-based index to monitor the depth of anesthesia

Development of EEG-based stress index

Similar Documents

EEG-based Stress Features Using Spectral Centroids Technique and k-Nearest Neighbor Classifier

Stress features identification from EEG signals using EEG Asymmetry & Spectral Centroids techniques

Emotional identification during mobile RF Radiation

Development of Brainwave Balancing Index Using EEG

Classification of frontal alpha asymmetry using k-Nearest Neighbor

Brainwave sub-band power ratio characteristics in intelligence assessment

Brainwaves stress pattern based on perceived stress scale test

Brainwave Classification for Acute Ischemic Stroke Group Level Using k-NN Technique

Presenting an effective EEG-based index to monitor the depth of anesthesia

Development of EEG-based stress index