Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 213-219 |
| Number of pages | 7 |
| Journal | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Volume | 13 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 1 2005 |
Keywords
- 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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Cite this
Sanchez, J. C., Erdogmus, D., Nicolelis, M. A. L., Wessberg, J., & Principe, J. C. (2005). Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(2), 213-219. https://doi.org/10.1109/TNSRE.2005.847382