Cortical control of reach and grasp kinematics in a virtual environment using musculoskeletal modeling software

Recently there has been a major initiative to develop a Brain-Machine Interface (BMI) for dexterous control of an upper-limb neuroprosthesis. This paper describes the use of a virtual environment using Musculoskeletal Modeling Software as a model system to test and evaluate cortical algorithms for predicting reach and grasp kinematics. Simultaneous neural and motion tracking data was acquired from a non-human primate trained to perform a center-out reach-and-grasp task. A Kalman Filter was designed to simultaneously predict kinematics of the arm, hand, and fingers with high accuracy (avg r=0.83; avg RMSE=13.7%). In lieu of an advanced mechanical limb, the decoded output was used to manipulate a fully articulated 18-DoF arm in a virtual environment using MSMS. This platform lays the foundation for future closed-loop experiments with non-human primates to demonstrate a BMI for dexterous control of the hand and fingers.