Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface.
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.
Sanchez
Justin C
JC
Department of Pediatrics Division of Neurology, University of Florida, Gainesville, FL 32611, USA. justin@cnel.ufl.edu
Erdogmus
Deniz
D
Nicolelis
Miguel A L
MA
Wessberg
Johan
J
Principe
Jose C
JC
D000465:Algorithms; D000818:Animals; D016645:Aotidae; D001185:Artificial Intelligence; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005246:Feedback; D006225:Hand / Q000294:innervation / Q000502:physiology; D009068:Movement / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors; D014584:User-Computer Interface