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Decoding brain states using functional brain imaging techniques

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TitleInfo
Title
Decoding brain states using functional brain imaging techniques
Name (type = personal)
NamePart (type = family)
Peifer
NamePart (type = given)
Maria
DisplayForm
Maria Peifer
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Najafizadeh
NamePart (type = given)
Laleh
DisplayForm
Laleh Najafizadeh
Affiliation
Advisory Committee
Role
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chair
Name (type = personal)
NamePart (type = family)
Petropulu
NamePart (type = given)
Athina
DisplayForm
Athina Petropulu
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Dana
NamePart (type = given)
Kristin
DisplayForm
Kristin Dana
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (qualifier = exact)
2015
DateOther (qualifier = exact); (type = degree)
2015-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2015
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Non-invasive neuroimaging techniques provide safe methods for investigating the func- tionality of the brain. Functional near infrared spectroscopy (fNIRS) is a non invasive brain imaging method, which uses light in the near infrared range to measure the changes in concentration of cerebral hemoglobin. Electroencephalography (EEG) is a noninvasive brain imaging technique that measures regional cortical activity by mea- suring the potential difference at various points on the surface of the scalp. In this work the two brain imaging techniques are used to decode brain states, using a paradigm for three conditions: rest, motor and motor imagery. The first part of the study attempts the classification of motor and motor imagery by using least square support vector machine (LS-SVM) with a radial basis function kernel. The data was recorded using functional near infrared spectroscopy. All pre- processing methods are selected to be possible for execution in a real-time setting. The first goal was to determine the optimal window length and starting point for the extraction of features. Once the optimal window length was established, two feature selection methods were compared: Fisher discriminant ratio (FDR) and the combined method, which uses FDR and K-means. Reducing the number of features improved the classification time with negligible impact on the classification accuracy. The second part of the study uses a LS-SVM with a linear kernel to perform two classifications on EEG data: rest and motor imagery, and rest and motor. The average power of the frequency band between 10 Hz − 14 Hz was used to extract features from each channel. The two feature selection methods previously mentioned w
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_6772
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (viii, 68 p. : ill.)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Machine learning
Subject (authority = ETD-LCSH)
Topic
Brain--Imaging
Note (type = statement of responsibility)
by Maria Peifer
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T34B3396
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Peifer
GivenName
Maria
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2015-09-24 11:27:04
AssociatedEntity
Name
Maria Peifer
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
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