Artifacts classification in EEG signals based on temporal average statistics

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 E-ISSN 2180-3722

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Abstract

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

Item Type:	Article (Journal)
Additional Information:	6145/47342
Uncontrolled Keywords:	EEG classification, EEG artifacts, statistical features, temporal averages, multi layer perceptron
Subjects:	UNSPECIFIED
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Prof Abdul Wahab Abdul Rahman
Date Deposited:	04 Jan 2016 13:56
Last Modified:	15 Jul 2016 09:15
URI:	http://irep.iium.edu.my/id/eprint/47342

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