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Artifacts classification in EEG signals based on temporal average statistics

EEG data contamination due to artifacts, such as eye blink, muscle activity, body movement and others pose as an issue in EEG analysis. This study aims to classify three different types of artifacts in EEG signal, namely; ocular, facial muscle and hand movement using statistical features coupled wit...

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Main Authors: Abdul, Qayoom, Abdul Rahman, Abdul Wahab, Kamaruddin, Norhaslinda, Zahid, Zahid
Format: Article
Language:English
Published: Universiti Teknologi Malaysia 2015
Online Access:http://irep.iium.edu.my/47342/1/6251-17392-1-SM.pdf
http://irep.iium.edu.my/47342/
http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/6251
http://dx.doi.org/10.11113/jt.v77.6251
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spelling my.iium.irep.473422016-07-15T01:15:19Z http://irep.iium.edu.my/47342/ Artifacts classification in EEG signals based on temporal average statistics Abdul, Qayoom Abdul Rahman, Abdul Wahab Kamaruddin, Norhaslinda Zahid, Zahid EEG data contamination due to artifacts, such as eye blink, muscle activity, body movement and others pose as an issue in EEG analysis. This study aims to classify three different types of artifacts in EEG signal, namely; ocular, facial muscle and hand movement using statistical features coupled with neural networks as classifier. Temporal averages of five features are used as the feature vector for MLP classification. The experimental results for ocular, facial muscle and hand movement artifacts identification are ranging between 80% and 92%. The classification accuracy for the combination of these EEG artifacts and normal EEG of the subject for resting and eyesclose state are 86% and 96% respectively Universiti Teknologi Malaysia 2015 Article REM application/pdf en http://irep.iium.edu.my/47342/1/6251-17392-1-SM.pdf Abdul, Qayoom and Abdul Rahman, Abdul Wahab and Kamaruddin, Norhaslinda and Zahid, Zahid (2015) Artifacts classification in EEG signals based on temporal average statistics. Jurnal Teknologi, 77 (7). pp. 73-77. ISSN 0127-9696 http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/6251 http://dx.doi.org/10.11113/jt.v77.6251
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
description EEG data contamination due to artifacts, such as eye blink, muscle activity, body movement and others pose as an issue in EEG analysis. This study aims to classify three different types of artifacts in EEG signal, namely; ocular, facial muscle and hand movement using statistical features coupled with neural networks as classifier. Temporal averages of five features are used as the feature vector for MLP classification. The experimental results for ocular, facial muscle and hand movement artifacts identification are ranging between 80% and 92%. The classification accuracy for the combination of these EEG artifacts and normal EEG of the subject for resting and eyesclose state are 86% and 96% respectively
format Article
author Abdul, Qayoom
Abdul Rahman, Abdul Wahab
Kamaruddin, Norhaslinda
Zahid, Zahid
spellingShingle Abdul, Qayoom
Abdul Rahman, Abdul Wahab
Kamaruddin, Norhaslinda
Zahid, Zahid
Artifacts classification in EEG signals based on temporal average statistics
author_facet Abdul, Qayoom
Abdul Rahman, Abdul Wahab
Kamaruddin, Norhaslinda
Zahid, Zahid
author_sort Abdul, Qayoom
title Artifacts classification in EEG signals based on temporal average statistics
title_short Artifacts classification in EEG signals based on temporal average statistics
title_full Artifacts classification in EEG signals based on temporal average statistics
title_fullStr Artifacts classification in EEG signals based on temporal average statistics
title_full_unstemmed Artifacts classification in EEG signals based on temporal average statistics
title_sort artifacts classification in eeg signals based on temporal average statistics
publisher Universiti Teknologi Malaysia
publishDate 2015
url http://irep.iium.edu.my/47342/1/6251-17392-1-SM.pdf
http://irep.iium.edu.my/47342/
http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/6251
http://dx.doi.org/10.11113/jt.v77.6251
_version_ 1643613173043953664
score 13.211869

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