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Patrick L. Purdon. Beck, Amanda M Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2019-02-14T15:48:54Z 2019-02-14T15:48:54Z 2018 2018 http://hdl.handle.net/1721.1/120408 1083780377 Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 55-56). Information communication in the brain depends on the spiking patterns of neurons. The interaction of these cells at the population level can be observed as oscillations of varying frequency and power, in local field potential recordings as well as non-invasive scalp electroencephalograms (EEG). These oscillations are thought to be responsible for coordinating activity across larger brain regions and conveying information across the brain, directing processes such as attention, consciousness, sensory and information processing. A common approach for analyzing these electrical potentials is to apply a band pass filter in the frequency band of interest. Canonical frequency bands have been defined and applied in many previous studies, but their specific definitions vary within the field, and are to some degree arbitrary. We propose an alternative approach that uses state space models to represent basic physiological and dynamic principles, whose detailed structure and parameterization are informed by observed data. We find that this method can more accurately represent oscillatory power, effectively separating it from background broadband noise power. This approach provides a way of separating oscillations in the time domain and while also quantifying their structure efficiently with a small number of parameters. by Amanda M. Beck. S.M. in Computer Science and Engineering 56 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. State space models for isolating neural oscillations Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3bb56d14-527c-4d40-a8ec-702d976941f0"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>State space models for isolating neural oscillations</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Beck, Amanda M</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Information communication in the brain depends on the spiking patterns of neurons. The interaction of these cells at the population level can be observed as oscillations of varying frequency and power, in local field potential recordings as well as non-invasive scalp electroencephalograms (EEG). These oscillations are thought to be responsible for coordinating activity across larger brain regions and conveying information across the brain, directing processes such as attention, consciousness, sensory and information processing. A common approach for analyzing these electrical potentials is to apply a band pass filter in the frequency band of interest. Canonical frequency bands have been defined and applied in many previous studies, but their specific definitions vary within the field, and are to some degree arbitrary. We propose an alternative approach that uses state space models to represent basic physiological and dynamic principles, whose detailed structure and parameterization are informed by observed data. We find that this method can more accurately represent oscillatory power, effectively separating it from background broadband noise power. This approach provides a way of separating oscillations in the time domain and while also quantifying their structure efficiently with a small number of parameters.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>