Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface.

Published

Journal Article

We propose the use of optimized brain-machine interface (BMI) models for interpreting the spatial and temporal neural activity generated in motor tasks. In this study, a nonlinear dynamical neural network is trained to predict the hand position of primates from neural recordings in a reaching task paradigm. We first develop a method to reveal the role attributed by the model to the sampled motor, premotor, and parietal cortices in generating hand movements. Next, using the trained model weights, we derive a temporal sensitivity measure to asses how the model utilized the sampled cortices and neurons in real-time during BMI testing.

Full Text

Duke Authors

Cited Authors

  • Sanchez, JC; Erdogmus, D; Nicolelis, MAL; Wessberg, J; Principe, JC

Published Date

  • June 2005

Published In

Volume / Issue

  • 13 / 2

Start / End Page

  • 213 - 219

PubMed ID

  • 16003902

Pubmed Central ID

  • 16003902

International Standard Serial Number (ISSN)

  • 1534-4320

Digital Object Identifier (DOI)

  • 10.1109/TNSRE.2005.847382

Language

  • eng

Conference Location

  • United States