A case-study of using tensors in multi-way electroencephalograme (EEG) data analysis

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dc.contributor Halpern, David
dc.contributor Hadji, Layachi
dc.contributor Hoang, Nguyen
dc.contributor.advisor Sidje, Roger B.
dc.contributor.author Milligan-Williams, Essence
dc.date.accessioned 2020-09-30T17:24:48Z
dc.date.available 2020-09-30T17:24:48Z
dc.date.issued 2020
dc.identifier.other u0015_0000001_0003609
dc.identifier.other MilliganWilliams_alatus_0004M_14124
dc.identifier.uri http://ir.ua.edu/handle/123456789/7008
dc.description Electronic Thesis or Dissertation
dc.description.abstract Tensors are multi-dimensional arrays that can represent large datasets. Acquiring large data sets has its pros and cons; a pro being the bigger the data set, the more information could potentially be generated. A con would be the amount of labor needed to process this information. To combat this con, researchers in a variety of fields rely on tensor decomposition. Tensor decomposition's goal is to compress the data without losing any signficant information. Tensor decompositon is also known for its ability to extract underlying features that could not have been seen at face value. One such field is electroencephalography, which is the study of electrograms (EEG). An electrogram is a brain imaging tool that measures brain electrical activity. Having the abilitiy to be continously recorded for long periods of time, this could be hours, days, even weeks, EEG tends to have massive multi-dimensional datasets. In order to process the data, tensor decompositon methods such as Parallel Factor Analysis (PARAFAC) and Tucker decomposition can be executed on these large datasets.
dc.format.extent 57 p.
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.ispartof The University of Alabama Libraries Digital Collections
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated.
dc.subject.other Applied mathematics
dc.title A case-study of using tensors in multi-way electroencephalograme (EEG) data analysis
dc.type thesis
dc.type text
etdms.degree.department University of Alabama. Department of Mathematics
etdms.degree.discipline Mathematics
etdms.degree.grantor The University of Alabama
etdms.degree.level master's
etdms.degree.name M.A.

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