Dry-EEG in Multicultural Pain Assessment

dc.contributor.advisorOmurtag, Ahmet
dc.contributor.committeeMemberCrawford, Malachi D.
dc.contributor.committeeMemberChen, Ting Y.
dc.contributor.committeeMemberGifford, Howard C.
dc.creatorOkolo, Chika
dc.creator.orcid0000-0002-9951-0478
dc.date.accessioned2019-09-18T00:15:42Z
dc.date.available2019-09-18T00:15:42Z
dc.date.createdAugust 2017
dc.date.issued2017-08
dc.date.submittedAugust 2017
dc.date.updated2019-09-18T00:15:42Z
dc.description.abstractThe primary purpose of this thesis is to design an experiment, which can be used for quantifying pain. This was made possible by using dry electroencephalography (EEG) in conjunction with a Support Vector Machines classifier (SVM). Normal gel-based electrode EEG has been validated as a reliable tool for objectively measuring pain. Yet, to date there are few documented trials, which use dry-EEG for pain quantification. SVM classifiers have been proved accurate, when classifying pain intensity. Therefore, we believe EEG combined with SVM could increase statistical power in pain classification. Due to the subjectivity of pain, currently clinicians mainly rely on the patient self-report. In addition, hyper/low sensitivity to pain may vary in certain cultures and backgrounds, which may be perceived as drug seeking behavior by clinicians. Therefore, this research could offer the tools needed to objectively describe pain, eliminate observer error, and individualize treatment based on background and culture.
dc.description.departmentBiomedical Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/4772
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectElectroencephalography (EEG)
dc.subjectElectroencephalogram
dc.subjectSupport Vector Machines
dc.subjectSupervised Machine Learning
dc.subjectPain experience
dc.subjectMinorities
dc.subjectHealth
dc.subjectSocioeconomic pain differences
dc.titleDry-EEG in Multicultural Pain Assessment
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentBiomedical Engineering, Department of
thesis.degree.disciplineBiomedical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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