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

Justin C. Sanchez, Deniz Erdogmus, Miguel A L Nicolelis, Johan Wessberg, Jose C. Principe

Research output: Contribution to journalArticle

33 Scopus citations


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.

Original languageEnglish
Pages (from-to)213-219
Number of pages7
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number2
StatePublished - Jun 1 2005
Externally publishedYes



  • Analysis of neural activity
  • Brain-machine interface (BMI)
  • Motor systems
  • Nonlinear models
  • Recurrent neural network
  • Spatio-temporal

ASJC Scopus subject areas

  • Rehabilitation
  • Biophysics
  • Bioengineering
  • Health Professions(all)

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