Boosted Dynamic Cognitive Activity Recognition from Brain Images

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Show simple item record Li, J Tao, D
dc.contributor.editor Draghici, S
dc.contributor.editor Khoshgoftaar, TM
dc.contributor.editor Palade, V
dc.contributor.editor Pedrycz, W
dc.contributor.editor Wani, MA
dc.contributor.editor Zhu, X 2012-02-02T11:11:58Z 2010-01
dc.identifier.citation Proceedings - The 9th International Conference on Machine Learning and Applications, ICMLA 2010, 2010, pp. 361 - 366
dc.identifier.isbn 978-0-7695-4300-0
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.
dc.publisher IEEE
dc.relation.isbasedon 10.1109/ICMLA.2010.60
dc.title Boosted Dynamic Cognitive Activity Recognition from Brain Images
dc.type Conference Proceeding
dc.parent Proceedings - The 9th International Conference on Machine Learning and Applications, ICMLA 2010
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.conference International Conference on Machine Learning and Applications
dc.for 170203 Knowledge Representation and Machine Learning
dc.personcode 111727
dc.personcode 111502
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 2010-12-12
dc.location.activity Washington, D.C., USA en_US
dc.location.activity ISI:000266708100002
dc.description.keywords Cognitive Activity Recognition, Functional Magnetic Resonance Imaging, Random Fields en_US
dc.description.keywords adolescent health
dc.description.keywords adolescents
dc.description.keywords literature review
dc.description.keywords nursing education
dc.description.keywords sleep
dc.description.keywords sleep disturbance
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)

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