Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/34157
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dc.contributor.authorSaidatul Ardeenawatie, Awang-
dc.contributor.authorPandiyan, Paulraj Murugesa, Prof. Dr.-
dc.contributor.authorSazali, Yaacob, Prof. Dr.-
dc.date.accessioned2014-04-29T04:40:36Z-
dc.date.available2014-04-29T04:40:36Z-
dc.date.issued2013-01-
dc.identifier.citationp. 201-204en_US
dc.identifier.isbn978-146734601-6-
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6481149-
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/34157-
dc.descriptionProceeding of 7th International Conference on Intelligent Systems and Control 2013 (ISCO 2013) at Coimbatore, Tamilnadu, India on 4 January 2013 through 5 January 2013.en_US
dc.description.abstractThis paper presented the possibility of implementing eigenvector methods to represent the features of electroencephalogram signal. In this study, three eigenvector methods were investigated namely Pisarenko, Multiple Signal Classification (MUSIC) and Modified Covariance. The ability of the features in representing good character of signal in order to discriminate two different EEG signals for relaxation and writing signal were tested using neural network. The power level obtained by eigenvector methods of the EEG signals were used as inputs of the neural network trained with Levenberg-Marquardt algorithm. The classification result shows that Modified Covariance method is a better technique to extract features for relaxation-writing task.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartofseriesProceeding of 7th International Conference on Intelligent Systems and Control 2013 (ISCO 2013);-
dc.subjectEEG signalen_US
dc.subjectModified Covarianceen_US
dc.subjectMUSICen_US
dc.subjectNeural Networken_US
dc.subjectPisarenkoen_US
dc.subjectPower Spectral Densityen_US
dc.titleImplementing eigen features methods/neural network for EEG signal analysisen_US
dc.typeWorking Paperen_US
dc.contributor.urlsaidatul@unimap.edu.myen_US
dc.contributor.urlpaul@unimap.edu.myen_US
dc.contributor.urls.yaacob@unimap.edu.myen_US
Appears in Collections:Conference Papers
Sazali Yaacob, Prof. Dr.
Paulraj Murugesa Pandiyan, Assoc. Prof. Dr.

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