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

Author information

1
Department of Pediatrics Division of Neurology, University of Florida, Gainesville, FL 32611, USA. justin@cnel.ufl.edu

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.

PMID:
16003902
DOI:
10.1109/TNSRE.2005.847382
[Indexed for MEDLINE]
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