High-dimensional classification for brain decoding

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dc.contributor.authorCroteau, Nicole Samantha
dc.contributor.supervisorNathoo, Farouk
dc.date.accessioned2015-08-26T18:11:50Z
dc.date.available2015-08-26T18:11:50Z
dc.date.copyright2015en_US
dc.date.issued2015-08-26
dc.degree.departmentDepartment of Mathematics and Statisticsen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractBrain decoding involves the determination of a subject’s cognitive state or an associated stimulus from functional neuroimaging data measuring brain activity. In this setting the cognitive state is typically characterized by an element of a finite set, and the neuroimaging data comprise voluminous amounts of spatiotemporal data measuring some aspect of the neural signal. The associated statistical problem is one of classification from high-dimensional data. We explore the use of functional principal component analysis, mutual information networks, and persistent homology for examining the data through exploratory analysis and for constructing features characterizing the neural signal for brain decoding. We review each approach from this perspective, and we incorporate the features into a classifier based on symmetric multinomial logistic regression with elastic net regularization. The approaches are illustrated in an application where the task is to infer from brain activity measured with magnetoencephalography (MEG) the type of video stimulus shown to a subject.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/6564
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectbrain decodingen_US
dc.subjectneuroimagingen_US
dc.subjectfunctional principal component analysisen_US
dc.subjectnetwork analysisen_US
dc.subjectpersistent homologyen_US
dc.subjectclassificationen_US
dc.subjectmagnetoencephalography (MEG)en_US
dc.titleHigh-dimensional classification for brain decodingen_US
dc.typeThesisen_US

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