Deep Learning for Electroencephalography Spatial Interpolation

dc.contributor.advisorHuang, Yufei
dc.contributor.authorCorley, Isaac
dc.contributor.committeeMemberKrishnan, Ram
dc.contributor.committeeMemberJin, Yufang
dc.creator.orcidhttps://orcid.org/0000-0002-9273-7303
dc.date.accessioned2024-02-09T20:18:31Z
dc.date.available2024-02-09T20:18:31Z
dc.date.issued2019
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractThe advancement and commercialization of Brain Computer Interface (BCI) research utilizing Electroencephalography (EEG) sensors is hindered by high cost and algorithm compatibility issues. Simply put, an algorithm requiring a specific number of EEG channels is incompatible with any headset with a lesser number of sensors. Current spatial interpolation methods are only commonly used for recreation of a single or few malfunctioning sensors and not known to perform well when recreating numerous channels. We prove through experiments that generative Deep Neural Network (DNN) based models can be trained to intelligently upsample the spatial resolution of a headset by recreating the necessary missing signals solely from the known low spatial resolution signals.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent49 pages
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12588/3092
dc.languageen
dc.subjectbrain computer interface
dc.subjectdeep learning
dc.subjectelectroencephalography
dc.subjectgenerative adversarial networks
dc.subjectneural networks
dc.subjectsuper resolution
dc.subject.classificationElectrical engineering
dc.subject.classificationBioinformatics
dc.subject.classificationComputer science
dc.titleDeep Learning for Electroencephalography Spatial Interpolation
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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