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dc.contributor.authorCordeiro De Amorim, Renato
dc.contributor.authorMirkin, Boris
dc.contributor.authorQ. Gan, John
dc.date.accessioned2017-06-22T13:13:52Z
dc.date.available2017-06-22T13:13:52Z
dc.date.issued2009
dc.identifier.citationCordeiro De Amorim , R , Mirkin , B & Q. Gan , J 2009 , ' A method for classifying mental tasks in the space of EEG transforms ' , Paper presented at UK Workshop on Computational Intelligence , Nottingham , United Kingdom , 7/09/09 - 9/09/09 .
dc.identifier.citationworkshop
dc.identifier.otherPURE: 9822528
dc.identifier.otherPURE UUID: 617a1278-9a60-483e-912c-406f9ddc29fd
dc.identifier.urihttp://hdl.handle.net/2299/18455
dc.descriptionRenato Cordeiro De Amorim, Boris Mirkin, John Q. Gan, ‘A method for classifying mental tasks in the space of EEG transforms’, paper presented at the UK Workshop on Computational Intelligence, Nottingham, UK, 7-9 September, 2009.
dc.description.abstractIn this article we describe a new method for supervised classification of EEG signals. This method applies to the power spectrum density data and assigns class-dependent information weights to individual pixels, so that the decision is defined by the summary weights of the most informative pixel features. We experimentally analyze several versions of the approach. The informative features appear to be rather similar among different individuals, thus supporting the view that there are subject independent general brain patterns for the same mental tasken
dc.language.isoeng
dc.titleA method for classifying mental tasks in the space of EEG transformsen
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
rioxxterms.versionAM
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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