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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Sanchez, JC; Principe, JC; Carmena, JM; Lebedev, MA; Nicolelis, MAL

Published Date

  • 2004

Published In

Volume / Issue

  • 2004 /

Start / End Page

  • 5321 - 5324

PubMed ID

  • 17271543

International Standard Serial Number (ISSN)

  • 1557-170X

Digital Object Identifier (DOI)

  • 10.1109/IEMBS.2004.1404486

Language

  • eng

Conference Location

  • United States