EEG analysis on actual and imaginary left and right hand lifting using Support Vector Machine (SVM) / Nabilah Hamzah .. [et al.]

Brain Computer Interface or BCI is a technology that creates new communication channel where human brain (via Electroencephalography) can communicate with electronic devices.EEG signal is produced by the neurons, where every thought, emotion and movement can generate different patterns of EEG signal...

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Main Authors: Hamzah, Nabilah, Zaini, Norliza, Sani, Maizura, Ismail, Nurlaila
Format: Article
Language:English
Published: Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam 2017
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Online Access:http://ir.uitm.edu.my/id/eprint/29587/1/29587.pdf
http://ir.uitm.edu.my/id/eprint/29587/
https://jeesr.uitm.edu.my/
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spelling my.uitm.ir.295872020-04-17T13:19:33Z http://ir.uitm.edu.my/id/eprint/29587/ EEG analysis on actual and imaginary left and right hand lifting using Support Vector Machine (SVM) / Nabilah Hamzah .. [et al.] Hamzah, Nabilah Zaini, Norliza Sani, Maizura Ismail, Nurlaila Computer engineering. Computer hardware Brain Computer Interface or BCI is a technology that creates new communication channel where human brain (via Electroencephalography) can communicate with electronic devices.EEG signal is produced by the neurons, where every thought, emotion and movement can generate different patterns of EEG signal. There are two objectives defined for this research. The first objective is to compare the EEG data generated for actual and imaginary motor movement when lifting the left and right hand by using Support Vector Machine (SVM).The second objective is to find the correlation in EEG pattern between the actual motor movement and imaginary motor movement data, which is also based on SVM classification analysis. From the classification analysis, the accuracy for actual left and right-hand lifting movement is obtained at 90%. Meanwhile, the accuracy for classifying EEG data of imaginary left and right-hand lifting movement is obtained at 75%. In finding the correlation between the actual and imaginary EEG data, a classification analysis is also done by combining the actual and imaginary data. In this experiment, the accuracy in classifying the left and right-hand lifting activities is obtained at 78.8%. The significant accuracy measures obtained means that there is some correlation in EEG patterns between the actual motor movement and imaginary motor movement of lifting either left or right hand. Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam 2017-06 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/29587/1/29587.pdf Hamzah, Nabilah and Zaini, Norliza and Sani, Maizura and Ismail, Nurlaila (2017) EEG analysis on actual and imaginary left and right hand lifting using Support Vector Machine (SVM) / Nabilah Hamzah .. [et al.]. Journal of Electrical and Electronic Systems Research (JEESR), 10. ISSN 1985-5389 https://jeesr.uitm.edu.my/
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Computer engineering. Computer hardware
spellingShingle Computer engineering. Computer hardware
Hamzah, Nabilah
Zaini, Norliza
Sani, Maizura
Ismail, Nurlaila
EEG analysis on actual and imaginary left and right hand lifting using Support Vector Machine (SVM) / Nabilah Hamzah .. [et al.]
description Brain Computer Interface or BCI is a technology that creates new communication channel where human brain (via Electroencephalography) can communicate with electronic devices.EEG signal is produced by the neurons, where every thought, emotion and movement can generate different patterns of EEG signal. There are two objectives defined for this research. The first objective is to compare the EEG data generated for actual and imaginary motor movement when lifting the left and right hand by using Support Vector Machine (SVM).The second objective is to find the correlation in EEG pattern between the actual motor movement and imaginary motor movement data, which is also based on SVM classification analysis. From the classification analysis, the accuracy for actual left and right-hand lifting movement is obtained at 90%. Meanwhile, the accuracy for classifying EEG data of imaginary left and right-hand lifting movement is obtained at 75%. In finding the correlation between the actual and imaginary EEG data, a classification analysis is also done by combining the actual and imaginary data. In this experiment, the accuracy in classifying the left and right-hand lifting activities is obtained at 78.8%. The significant accuracy measures obtained means that there is some correlation in EEG patterns between the actual motor movement and imaginary motor movement of lifting either left or right hand.
format Article
author Hamzah, Nabilah
Zaini, Norliza
Sani, Maizura
Ismail, Nurlaila
author_facet Hamzah, Nabilah
Zaini, Norliza
Sani, Maizura
Ismail, Nurlaila
author_sort Hamzah, Nabilah
title EEG analysis on actual and imaginary left and right hand lifting using Support Vector Machine (SVM) / Nabilah Hamzah .. [et al.]
title_short EEG analysis on actual and imaginary left and right hand lifting using Support Vector Machine (SVM) / Nabilah Hamzah .. [et al.]
title_full EEG analysis on actual and imaginary left and right hand lifting using Support Vector Machine (SVM) / Nabilah Hamzah .. [et al.]
title_fullStr EEG analysis on actual and imaginary left and right hand lifting using Support Vector Machine (SVM) / Nabilah Hamzah .. [et al.]
title_full_unstemmed EEG analysis on actual and imaginary left and right hand lifting using Support Vector Machine (SVM) / Nabilah Hamzah .. [et al.]
title_sort eeg analysis on actual and imaginary left and right hand lifting using support vector machine (svm) / nabilah hamzah .. [et al.]
publisher Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam
publishDate 2017
url http://ir.uitm.edu.my/id/eprint/29587/1/29587.pdf
http://ir.uitm.edu.my/id/eprint/29587/
https://jeesr.uitm.edu.my/
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score 13.211869