Boosted Dynamic Cognitive Activity Recognition from Brain Images

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Show simple item record Li, Jun en_US Tao, Dacheng en_US
dc.contributor.editor Draghici, Sorin; Khoshgoftaar, Taghi M.; Palade, Vasile; Pedrycz, Witold; Wani, M. Arif; Zhu, Xingquan en_US 2012-02-02T11:11:58Z 2012-02-02T11:11:58Z 2010 en_US
dc.identifier 2010001532 en_US
dc.identifier.citation Li Jun and Tao Dacheng 2010, 'Boosted Dynamic Cognitive Activity Recognition from Brain Images', , IEEE, USA, , pp. 361-366. en_US
dc.identifier.issn 978-0-7695-4300-0 en_US
dc.identifier.other E1 en_US
dc.description.abstract Functional Magnetic Resonance Imaging (fMRI) has become an important diagnostic tool for measuring brain haemodynamics. Previous research on analysing fMRI data mainly focuses on detecting low-level neuron activation from the ensued haemodynamic activities. An important recent advance is to show that the high-level cognitive status is recognisable from a period of fMRI records. Nevertheless, it would also be helpful to reveal dynamics of cognitive activities during the period. In this paper, we tackle the problem of discovering the dynamic cognitive activities by proposing an algorithm of boosted structure learning. We employ statistic model of random fields to represent the dynamics of the brain. To exploit the rich fMRI observations with reasonable model complexity, we build multiple models, where one model links the cognitive activities to only a fraction of the fMRI observations. We combine the simple models by using an altered AdaBoost scheme for multi-class structure learning and show theoretical justification of the proposed scheme. Empirical test shows the method effectively links the physiological and the psychological activities of the brain. en_US
dc.language English en_US
dc.publisher IEEE en_US
dc.relation.isbasedon en_US
dc.title Boosted Dynamic Cognitive Activity Recognition from Brain Images en_US
dc.parent Proceedings - The 9th International Conference on Machine Learning and Applications, ICMLA 2010 en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 361 en_US
dc.identifier.endpage 366 en_US FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 170203 en_US
dc.personcode 111727 en_US
dc.personcode 111502 en_US
dc.percentage 100 en_US Knowledge Representation and Machine Learning en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Conference on Machine Learning and Applications en_US 20101212 en_US
dc.location.activity Washington, D.C., USA en_US
dc.description.keywords Cognitive Activity Recognition, Functional Magnetic Resonance Imaging, Random Fields en_US
dc.staffid en_US
dc.staffid 111502 en_US

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