Repository logo
Log in(current)
Repository logoMIT Open ScholarshipDSpace@MIT
  1. Home
  2. MIT Open Access Articles
  3. MIT Open Access Articles
  4. A parallel point-process filter for estimation of goal-directed movements from neural signals

A parallel point-process filter for estimation of goal-directed movements from neural signals

Thumbnail Image
Download
Name

Brown-2010-A parallel point-process filter for estimation.pdf

Size

235.49 KB

Format

Adobe PDF

Checksum (MD5)

2f4d63a89fa13df10548e174a9d3e807

Author(s)
Modir Shanechi, Maryam
•
Wornell, Gregory W.
•
Williams, Ziv
•
Brown, Emery N.
Date Issued
June 2010
Journal
Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)
Publisher
Institute of Electrical and Electronics Engineers
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.
Version
Final published version
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%.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
Persistent DSpace Link
http://hdl.handle.net/1721.1/70535
DOI of Published Version
https://doi.org/10.1109/ICASSP.2010.5495644
Repository logo
PrivacyPermissionsAccessibilityContact us
Repository logo
Notify us about copyright concerns.