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

doi:10.1109/tnsre.2005.847382

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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

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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.

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Algorithms Animals Aotidae Artificial Intelligence Brain Mapping Cerebral Cortex Diagnosis, Computer-Assisted Electroencephalography Evoked Potentials, Motor Feedback Hand Movement Neural Networks, Computer Neurons Pattern Recognition, Automated Time Factors User-Computer Interface Journal Article Research Support, U.S. Gov't, Non-P.H.S.

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Justin C Sanchez, Deniz Erdogmus, Miguel A L Nicolelis, Johan Wessberg, Jose C Principe. 2005. Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface.. https://doi.org/10.1109/tnsre.2005.847382

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