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dc.contributor.advisorBlunt, Shannon
dc.contributor.authorChan, Tsz Ping
dc.date.accessioned2008-09-15T03:16:14Z
dc.date.available2008-09-15T03:16:14Z
dc.date.issued2008-07-25
dc.date.submitted2008
dc.identifier.otherhttp://dissertations.umi.com/ku:2674
dc.identifier.urihttp://hdl.handle.net/1808/4165
dc.description.abstractTwo novel approaches are developed for direction-of-arrival (DOA) estimation and functional brain imaging estimation, which are denoted as ReIterative Super-Resolution (RISR) and Source AFFine Image REconstruction (SAFFIRE), respectively. Both recursive approaches are based on a minimum mean-square error (MMSE) framework. The RISR estimator recursively determines an optimal filter bank by updating an estimate of the spatial power distribution at each successive stage. Unlike previous non-parametric covariance-based approaches, which require numerous time snapshots of data, RISR is a parametric approach thus enabling operation on as few as one time snapshot, thereby yielding very high temporal resolution and robustness to the deleterious effects of temporal correlation. RISR has been found to resolve distinct spatial sources several times better than that afforded by the nominal array resolution even under conditions of temporally correlated sources and spatially colored noise. The SAFFIRE algorithm localizes the underlying neural activity in the brain based on the response of a patient under sensory stimuli, such as an auditory tone. The estimator processes electroencephalography (EEG) or magnetoencephalography (MEG) data simulated for sensors outside the patient's head in a recursive manner converging closer to the true solution at each consecutive stage. The algorithm requires a minimal number of time samples to localize active neural sources, thereby enabling the observation of the neural activity as it progresses over time. SAFFIRE has been applied to simulated MEG data and has shown to achieve unprecedented spatial and temporal resolution. The estimation approach has also demonstrated the capability to precisely isolate the primary and secondary auditory cortex responses, a challenging problem in the brain MEG imaging community.
dc.format.extent121 pages
dc.language.isoEN
dc.publisherUniversity of Kansas
dc.rightsThis item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
dc.subjectElectronics and electrical engineering
dc.subjectAgricultural economics
dc.titleREITERATIVE MINIMUM MEAN SQUARE ERROR ESTIMATOR FOR DIRECTION OF ARRIVAL ESTIMATION AND BIOMEDICAL FUNCTIONAL BRAIN IMAGING
dc.typeThesis
dc.contributor.cmtememberPopescu, Mihai
dc.contributor.cmtememberStiles, Jim
dc.contributor.cmtememberAgah, Arvin
dc.thesis.degreeDisciplineElectrical Engineering & Computer Science
dc.thesis.degreeLevelM.S.
kusw.oastatusna
kusw.oapolicyThis item does not meet KU Open Access policy criteria.
kusw.bibid6857298
dc.rights.accessrightsopenAccess


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