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dc.contributor.authorCordeiro De Amorim, Renato
dc.contributor.authorMirkin, Boris
dc.contributor.authorQ. Gan, John
dc.date.accessioned2016-03-16T10:15:16Z
dc.date.available2016-03-16T10:15:16Z
dc.date.issued2012-09-01
dc.identifier.citationCordeiro De Amorim , R , Mirkin , B & Q. Gan , J 2012 , ' Anomalous pattern based clustering of mental tasks with subject independent learning : some preliminary results ' , Artificial Intelligence Research , vol. 1 , no. 1 . https://doi.org/10.5430/air.v1n1p55
dc.identifier.issn1927-6974
dc.identifier.otherPURE: 9822380
dc.identifier.otherPURE UUID: 069b6c4e-ad64-469b-8dac-997f836c1189
dc.identifier.urihttp://hdl.handle.net/2299/16806
dc.descriptionThis work is licensed under a Creative Commons Attribution 3.0 License
dc.description.abstractIn this paper we describe a new method for EEG signal classification in which the classification of one subject’s EEG signals is based on features learnt from another subject. This method applies to the power spectrum density data and assigns class-dependent information weights to individual features. The informative features appear to be rather similar among different subjects, thus supporting the view that there are subject independent general brain patterns for the same mental task. Classification is done via clustering using the intelligent k-means algorithm with the most informative features from a different subject. We experimentally compare our method with others.en
dc.language.isoeng
dc.relation.ispartofArtificial Intelligence Research
dc.titleAnomalous pattern based clustering of mental tasks with subject independent learning : some preliminary resultsen
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Computer Science
dcterms.dateAccepted2012-09-01
rioxxterms.versionofrecordhttps://doi.org/10.5430/air.v1n1p55
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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