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EEG classification of physiological conditions in 2D/3D environments using neural network

Higher classification accuracy is more desirable for brain computer interface (BCI) applications. The accuracy can be achieved by appropriate selection of relevant features. In this paper a new scheme is proposed based on six different nonlinear features. These features include Sample entropy (SampE...

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Main Authors: Mumtaz, W., Xia, L., Malik, A.S., Mohd Yasin, M.A.
Format: Conference or Workshop Item
Published: 2013
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886467593&doi=10.1109%2fEMBC.2013.6610480&partnerID=40&md5=ec2884cf099e80d3f8ecef996599a54a
http://eprints.utp.edu.my/32664/
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spelling my.utp.eprints.326642022-03-30T01:02:15Z EEG classification of physiological conditions in 2D/3D environments using neural network Mumtaz, W. Xia, L. Malik, A.S. Mohd Yasin, M.A. Higher classification accuracy is more desirable for brain computer interface (BCI) applications. The accuracy can be achieved by appropriate selection of relevant features. In this paper a new scheme is proposed based on six different nonlinear features. These features include Sample entropy (SampEn), Composite permutation entropy index (CPEI), Approximate entropy (ApEn), Fractal dimension (FD), Hurst exponent (H) and Hjorth parameters (complexity and mobility). These features are decision variables for classification of physiological conditions: Eyes Open (EO), Eyes Closed (EC), Game Playing 2D (GP2D), Game playing 3D active (GP3DA) and Game playing 3D passive (GP3DP). Results show that the scheme can successfully classify the conditions with an accuracy of 88.9. © 2013 IEEE. 2013 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886467593&doi=10.1109%2fEMBC.2013.6610480&partnerID=40&md5=ec2884cf099e80d3f8ecef996599a54a Mumtaz, W. and Xia, L. and Malik, A.S. and Mohd Yasin, M.A. (2013) EEG classification of physiological conditions in 2D/3D environments using neural network. In: UNSPECIFIED. http://eprints.utp.edu.my/32664/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Higher classification accuracy is more desirable for brain computer interface (BCI) applications. The accuracy can be achieved by appropriate selection of relevant features. In this paper a new scheme is proposed based on six different nonlinear features. These features include Sample entropy (SampEn), Composite permutation entropy index (CPEI), Approximate entropy (ApEn), Fractal dimension (FD), Hurst exponent (H) and Hjorth parameters (complexity and mobility). These features are decision variables for classification of physiological conditions: Eyes Open (EO), Eyes Closed (EC), Game Playing 2D (GP2D), Game playing 3D active (GP3DA) and Game playing 3D passive (GP3DP). Results show that the scheme can successfully classify the conditions with an accuracy of 88.9. © 2013 IEEE.
format Conference or Workshop Item
author Mumtaz, W.
Xia, L.
Malik, A.S.
Mohd Yasin, M.A.
spellingShingle Mumtaz, W.
Xia, L.
Malik, A.S.
Mohd Yasin, M.A.
EEG classification of physiological conditions in 2D/3D environments using neural network
author_facet Mumtaz, W.
Xia, L.
Malik, A.S.
Mohd Yasin, M.A.
author_sort Mumtaz, W.
title EEG classification of physiological conditions in 2D/3D environments using neural network
title_short EEG classification of physiological conditions in 2D/3D environments using neural network
title_full EEG classification of physiological conditions in 2D/3D environments using neural network
title_fullStr EEG classification of physiological conditions in 2D/3D environments using neural network
title_full_unstemmed EEG classification of physiological conditions in 2D/3D environments using neural network
title_sort eeg classification of physiological conditions in 2d/3d environments using neural network
publishDate 2013
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886467593&doi=10.1109%2fEMBC.2013.6610480&partnerID=40&md5=ec2884cf099e80d3f8ecef996599a54a
http://eprints.utp.edu.my/32664/
_version_ 1738657418455285760
score 13.211508

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