Modeling electroencephalography (EEG) in the frequency domain and the time domain

dc.contributor.advisorKannan, Nandini
dc.contributor.authorShi, Chao
dc.contributor.committeeMemberKannan, Nandini
dc.contributor.committeeMemberYe, Keying
dc.contributor.committeeMemberTripathi, Ram
dc.contributor.committeeMemberRobbins, Kay A.
dc.contributor.committeeMemberKundu, Debasis
dc.date.accessioned2024-03-08T15:43:24Z
dc.date.available2024-03-08T15:43:24Z
dc.date.issued2013
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.abstractOne of the important problems in neuroscience is determining the reliability of repeated EEG recordings over time. Quantifying the intra-individual stability/ variability in the recordings allows researchers to test for the effects of retention or learning for different types of subjects, as well as to examine the influence of external factors. In this dissertation, we consider models for <italic>R</italic> repeated EEG time series in both the time and frequency domains. We develop methodology to assess and quantify the reliability of the series. In the frequency domain, we propose three spectral closeness coefficients that quantify the closeness of the <italic>R</italic> spectra at individual frequencies and across specified frequency bands. These closeness measures are computed from the smoothed periodograms of the <italic>R</italic> EEG time series. Under certain assumptions, we derive the exact distribution of the spectral closeness coefficients <italic>scc</italic><sub>1</sub> and <italic>scc</italic><sub>2</sub> and compare their performance in terms of mean squared error. Based on the results, we propose a third closeness measure <italic>scc</italic><sub>3</sub> and investigate its properties across the entire frequency band [0:5; 30) Hz through a simulation study. In the time domain, we propose a sum-of-sinusoids model for the EEG time series. We develop algorithms to estimate the model parameters including the amplitudes, frequencies, phases, and the number of sinusoidal components. To compare the <italic>R</italic> series, we construct a likelihood ratio test (LRT) for testing the null hypothesis that all <italic>R</italic> series have the same parameters. The p-value of the LRT provides a measure of closeness of the <italic>R</italic> series based on the underlying parametric model and may be used to cluster the <italic>R</italic> series. We extend the single-channel model to the multi-channel case with suitable assumptions on the correlation structure in the spatial and time domains. We illustrate the performance of the proposed methods on two real EEG datasets. The results based on the spectral closeness coefficients show spectral features of high reliability that may be used as an input for a brain-computer interfacing (BCI) system. Using the LRT based on the sumof-sinusoids model, we can determine which of the <italic>R</italic> series on a single subject are similar, and also quantify the differences between subjects.
dc.description.departmentManagement Science and Statistics
dc.format.extent124 pages
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12588/5634
dc.languageen
dc.subjectEEG
dc.subjectlikelihood ratio test
dc.subjectspectral closeness coefficient
dc.subjectsum-of-sinusoids model
dc.subject.classificationStatistics
dc.titleModeling electroencephalography (EEG) in the frequency domain and the time domain
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentManagement Science and Statistics
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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