Towards closed-loop decoding of dexterous hand movements using a virtual integration environment

It has been shown that Brain-Computer Interfaces (BCIs) involving closed-loop control of an external device, while receiving visual feedback, allows subjects to adaptively correct errors and improve the accuracy of control. Although closed-loop cortical control of gross arm movements has been demonstrated, closed-loop decoding of more dexterous movements such as individual fingers has not been shown. Neural recordings were obtained from rhesus monkeys in three different experiments involving individuated flexion/extension of each finger, wrist rotation, and dexterous grasps. Separate decoding filters were implemented in Matlab's Simulink environment to independently decode this suite of dexterous movements in real-time. Average real-time decoding accuracies of >80% was achieved for all dexterous tasks with as few as 15 neurons for individual finger flexion/extension, 41 neurons for wrist rotation, and 79 neurons for grasps. In lieu of the availability of advanced multi-fingered prosthetic hands, real-time visual feedback of the decoded output was provided through actuation of a virtual prosthetic hand in a Virtual Integration Environment. This work lays the foundation for future closed-loop experiments with monkeys in the loop and dexterous control of an actual prosthetic limb.