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dc.contributor.authorModir Shanechi, Maryam
dc.contributor.authorWornell, Gregory W.
dc.contributor.authorWilliams, Ziv
dc.contributor.authorBrown, Emery N.
dc.date.accessioned2012-05-07T20:36:09Z
dc.date.available2012-05-07T20:36:09Z
dc.date.issued2010-06
dc.date.submitted2010-03
dc.identifier.isbn978-1-4244-4295-9
dc.identifier.issn1520-6149
dc.identifier.otherINSPEC Accession Number: 11553666
dc.identifier.urihttp://hdl.handle.net/1721.1/70535
dc.description.abstractBrain machine interfaces work by mapping the relevant neural activity to the intended movement known as 'decoding'. Here, we develop a recursive Bayesian decoder for goal-directed movements from neural observations, which exploits the optimal feedback control model of the sensorimotor system to build better prior state-space models. These controlled state models depend on the movement duration that is not known a priori. We thus consider a discretization of the task duration and develop a decoder consisting of a bank of parallel point-process filters, each combining the neural observation with the controlled state model of a discretization point. The final reconstruction is made by optimally combining these filter estimates. Using very coarse discretization and hence only a few parallel branches, our decoder reduces the root mean square (RMS) error in trajectory reconstruction in reaches made by a rhesus monkey by approximately 40%.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant No. DP1- 0D003646-01)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant R01-EB006385)en_US
dc.description.sponsorshipMicrosoft Researchen_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICASSP.2010.5495644en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceIEEEen_US
dc.titleA parallel point-process filter for estimation of goal-directed movements from neural signalsen_US
dc.typeArticleen_US
dc.identifier.citationShanechi, Maryam Modir et al. “A Parallel Point-process Filter for Estimation of Goal-directed Movements from Neural Signals.” IEEE, 2010. 521–524. Web. © 2010 IEEE.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.approverBrown, Emery N.
dc.contributor.mitauthorBrown, Emery N.
dc.contributor.mitauthorModir Shanechi, Maryam
dc.contributor.mitauthorWornell, Gregory W.
dc.contributor.mitauthorWilliams, Ziv
dc.relation.journalProceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)en_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsShanechi, Maryam Modir; Wornell, Gregory W.; Williams, Ziv; Brown, Emery N.en
dc.identifier.orcidhttps://orcid.org/0000-0003-2668-7819
dc.identifier.orcidhttps://orcid.org/0000-0001-9166-4758
mit.licensePUBLISHER_POLICYen_US


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