Simultaneous prediction of four kinematic variables for a brain-machine interface using a single recurrent neural network

J. C. Sanchez, J. C. Principe, J. M. Carmena, Mikhail A. Lebedev, M. A.L. Nicolelis

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations

Abstract

Implementation of brain-machine interface neural-to-motor mapping algorithms in low-power, portable digital signal processors (DSPs) requires efficient use of model resources especially when predicting signals that show interdependencies. We show here that a single recurrent neural network can simultaneously predict hand position and velocity from the same ensemble of cells using a minimalist topology. Analysis of the trained topology showed that the model learns to concurrently represent multiple kinematic parameters in a single state variable. We further assess the expressive power of the state variables for both large and small topologies.

Original languageEnglish (US)
Pages (from-to)5321-5324
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 VII
StatePublished - Dec 1 2004
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: Sep 1 2004Sep 5 2004

Keywords

  • Brain-Machine Interface
  • Neuroprosthetic
  • Recurrent neural network
  • RMLP
  • State-space analysis

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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