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.
Aggarwal
Vikram
V
Department of Biomedical Engineering at The Johns Hopkins University, Baltimore, MD, USA. vaggarwal@jhu.edu
Singhal
Girish
G
He
Jiping
J
Schieber
Marc H
MH
Thakor
Nitish V
NV
D000465:Algorithms; D000818:Animals; D001676:Biofeedback, Psychology; D001698:Biomedical Engineering; D001921:Brain / Q000502:physiology; D016208:Databases, Factual; D006225:Hand / Q000502:physiology*; D008253:Macaca mulatta; D008297:Male; D009068:Movement / Q000502:physiology*; D016571:Neural Networks, Computer; D012984:Software; D014584:User-Computer Interface