Dynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise

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dc.contributor.advisorJonathan, Simon
dc.contributor.advisorBabadi, Betash
dc.contributor.authorPresacco, Alessandro
dc.contributor.authorMiran, Sina
dc.contributor.authorFu, Michael
dc.contributor.authorMarcus, Steven
dc.date.accessioned2020-08-02T20:49:25Z
dc.date.available2020-08-02T20:49:25Z
dc.date.issued2020-08-02
dc.descriptionMEG data were collected from 157 sensors + 35 additional channels (158 - 192) used for reference and trigger. Each mat file contains information about the data, such as sampling frequency. A 3D matrix is used to store the 3 repetitions recorded.en_US
dc.description.abstractMEG data used for the "Switching attention" experimenten_US
dc.description.sponsorshipDefense Advanced Research Projects 736 Agency (DARPA) under cooperative agreement number N660011824024en_US
dc.identifierhttps://doi.org/10.13016/di6h-cav9
dc.identifier.citation"Dynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise", PLOS Computational Biology (2020), in pressen_US
dc.identifier.urihttp://hdl.handle.net/1903/26351
dc.language.isoenen_US
dc.publisherPLOS Computational Biologyen_US
dc.relation.isAvailableAtInstitute for Systems Researchen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectMEGen_US
dc.subjectDynamic switching of attentionen_US
dc.subjectState-Space Modelen_US
dc.titleDynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noiseen_US
dc.typeArticleen_US

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