Decoding a New Neural Machine Interface for Control of Artificial Limbs

• Published in: Journal of neurophysiology Vol. 98; no. 5; pp. 2974 - 2982
• Main Authors: Zhou, Ping, Lowery, Madeleine M, Englehart, Kevin B, Huang, He, Li, Guanglin, Hargrove, Levi, Dewald, Julius P. A, Kuiken, Todd A
• Format: Journal Article
• Language: English
• Published: United States Am Phys Soc 01.11.2007
• Subjects: Adult; Amputation - rehabilitation; Amputation Stumps - innervation; Artificial Limbs; Electromyography - methods; Female; Humans; Male; Middle Aged; Muscle, Skeletal - innervation; User-Computer Interface
• ISSN: 0022-3077; 1522-1598
• DOI: 10.1152/jn.00178.2007

1 Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago; 2 Department of Physical Medicine and Rehabilitation, 3 Department of Physical Therapy and Human Movement Sciences, and 4 Department of Biomedical Engineering, Northwestern University, Chicago, Illinois; 5 Institute of Biomedical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada; and 6 School of Electrical, Electronic and Mechanical Engineering, University College Dublin, Dublin, Ireland Submitted 16 February 2007; accepted in final form 26 August 2007 An analysis of the motor control information content made available with a neural–machine interface (NMI) in four subjects is presented in this study. We have developed a novel NMI–called targeted muscle reinnervation (TMR)—to improve the function of artificial arms for amputees. TMR involves transferring the residual amputated nerves to nonfunctional muscles in amputees. The reinnervated muscles act as biological amplifiers of motor commands in the amputated nerves and the surface electromyogram (EMG) can be used to enhance control of a robotic arm. Although initial clinical success with TMR has been promising, the number of degrees of freedom of the robotic arm that can be controlled has been limited by the number of reinnervated muscle sites. In this study we assess how much control information can be extracted from reinnervated muscles using high-density surface EMG electrode arrays to record surface EMG signals over the reinnervated muscles. We then applied pattern classification techniques to the surface EMG signals. High accuracy was achieved in the classification of 16 intended arm, hand, and finger/thumb movements. Preliminary analyses of the required number of EMG channels and computational demands demonstrate clinical feasibility of these methods. This study indicates that TMR combined with pattern-recognition techniques has the potential to further improve the function of prosthetic limbs. In addition, the results demonstrate that the central motor control system is capable of eliciting complex efferent commands for a missing limb, in the absence of peripheral feedback and without retraining of the pathways involved. Address for reprint requests and other correspondence: T. Kuiken, 345 East Superior Street, Room 1309, Chicago, IL 60611 (E-mail: tkuiken{at}northwestern.edu )