dc.contributor.author | Modir Shanechi, Maryam | |
dc.contributor.author | Wornell, Gregory W. | |
dc.contributor.author | Williams, Ziv | |
dc.contributor.author | Brown, Emery N. | |
dc.date.accessioned | 2012-05-07T20:36:09Z | |
dc.date.available | 2012-05-07T20:36:09Z | |
dc.date.issued | 2010-06 | |
dc.date.submitted | 2010-03 | |
dc.identifier.isbn | 978-1-4244-4295-9 | |
dc.identifier.issn | 1520-6149 | |
dc.identifier.other | INSPEC Accession Number: 11553666 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/70535 | |
dc.description.abstract | Brain 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.sponsorship | National Institutes of Health (U.S.) (Grant No. DP1- 0D003646-01) | en_US |
dc.description.sponsorship | National Institutes of Health (U.S.) (Grant R01-EB006385) | en_US |
dc.description.sponsorship | Microsoft Research | en_US |
dc.language.iso | en_US | |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/ICASSP.2010.5495644 | en_US |
dc.rights | Article 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.source | IEEE | en_US |
dc.title | A parallel point-process filter for estimation of goal-directed movements from neural signals | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Shanechi, 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.department | Harvard University--MIT Division of Health Sciences and Technology | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.contributor.approver | Brown, Emery N. | |
dc.contributor.mitauthor | Brown, Emery N. | |
dc.contributor.mitauthor | Modir Shanechi, Maryam | |
dc.contributor.mitauthor | Wornell, Gregory W. | |
dc.contributor.mitauthor | Williams, Ziv | |
dc.relation.journal | Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
dspace.orderedauthors | Shanechi, Maryam Modir; Wornell, Gregory W.; Williams, Ziv; Brown, Emery N. | en |
dc.identifier.orcid | https://orcid.org/0000-0003-2668-7819 | |
dc.identifier.orcid | https://orcid.org/0000-0001-9166-4758 | |
mit.license | PUBLISHER_POLICY | en_US |
mit.metadata.status | Complete | |