Noninvasive, Accurate Assessment of the Behavior of Representative Populations of Motor Units in Targeted Reinnervated Muscles

Targeted muscle reinnervation (TMR) redirects nerves that have lost their target, due to amputation, to remaining muscles in the region of the stump with the intent of establishing intuitive myosignals to control a complex prosthetic device. In order to directly recover the neural code underlying an attempted limb movement, in this paper, we present the decomposition of high-density surface electromyographic (EMG) signals detected from three TMR patients into the individual motor unit spike trains. The aim was to prove, for the first time, the feasibility of decoding the neural drive that would reach muscles of the missing limb in TMR patients, to show the accuracy of the decoding, and to demonstrate the representativeness of the pool of extracted motor units. Six to seven flexible EMG electrode grids of 64 electrodes each were mounted over the reinnervated muscles of each patient, resulting in up to 448 EMG signals. The subjects were asked to attempt elbow extension and flexion, hand open and close, wrist extension and flexion, wrist pronation and supination, of their missing limb. The EMG signals were decomposed using the Convolution Kernel Compensation technique and the decomposition accuracy was evaluated with a signal-based index of accuracy, called pulse-to-noise ratio (PNR). The results showed that the spike trains of 3 to 27 motor units could be identified for each task, with a sensitivity of the decomposition >90%, as revealed by PNR. The motor unit discharge rates were within physiological values of normally innervated muscles. Moreover, the detected motor units showed a high degree of common drive so that the set of extracted units per task was representative of the behavior of the population of active units. The results open a path for a new generation of human-machine interfaces in which the control signals are extracted from noninvasive recordings and the obtained neural information is based directly on the spike trains of motor neurons.

[1]  R. Enoka,et al.  Discharge variability of motor units in an intrinsic muscle of transplanted hand. , 2008, Journal of neurophysiology.

[2]  C J Heckman,et al.  Motor unit. , 2012, Comprehensive Physiology.

[3]  L. Lindstrom,et al.  Signal Processing for the Multistate Myoelectric Channel , 1977 .

[4]  Todd A. Kuiken,et al.  The Effect of ECG Interference on Pattern-Recognition-Based Myoelectric Control for Targeted Muscle Reinnervated Patients , 2009, IEEE Transactions on Biomedical Engineering.

[5]  R N Scott.  Myoelectric control of prostheses. , 1966, Archives of physical medicine and rehabilitation.

[6]  D. Farina,et al.  Estimating motor unit discharge patterns from high-density surface electromyogram , 2009, Clinical Neurophysiology.

[7]  M. Swiontkowski.  Targeted Muscle Reinnervation for Real-time Myoelectric Control of Multifunction Artificial Arms , 2010 .

[8]  T. Kuiken,et al.  Targeted Reinnervation for Transhumeral Amputees: Current Surgical Technique and Update on Results , 2009, Plastic and reconstructive surgery.

[9]  Kengo Ohnishi,et al.  Neural machine interfaces for controlling multifunctional powered upper-limb prostheses , 2007, Expert review of medical devices.

[10]  D. Farina,et al.  Fluctuations in isometric muscle force can be described by one linear projection of low‐frequency components of motor unit discharge rates , 2009, The Journal of physiology.

[11]  A Holobar,et al.  Non-invasive characterization of motor unit behaviour in pathological tremor , 2012, Journal of neural engineering.

[12]  R.N. Scott,et al.  A new strategy for multifunction myoelectric control , 1993, IEEE Transactions on Biomedical Engineering.

[13]  C. D. De Luca,et al.  Control scheme governing concurrently active human motor units during voluntary contractions , 1982, The Journal of physiology.

[14]  Kevin Englehart,et al.  Multifunction control of prostheses using the myoelectric signal , 2001 .

[15]  Roberto Merletti,et al.  The extraction of neural strategies from the surface EMG. , 2004, Journal of applied physiology.

[16]  Kevin C. McGill,et al.  EMGLAB: An interactive EMG decomposition program , 2005, Journal of Neuroscience Methods.

[17]  D. Farina,et al.  Experimental Analysis of Accuracy in the Identification of Motor Unit Spike Trains From High-Density Surface EMG , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  R. Enoka,et al.  Detecting the unique representation of motor-unit action potentials in the surface electromyogram. , 2008, Journal of neurophysiology.

[19]  Dario Farina,et al.  Myoelectric Control of Artificial Limbs¿Is There a Need to Change Focus? [In the Spotlight] , 2012, IEEE Signal Process. Mag..

[20]  Robert D. Lipschutz,et al.  Improved Myoelectric Prosthesis Control Accomplished Using Multiple Nerve Transfers , 2006, Plastic and reconstructive surgery.

[21]  O. Aszmann,et al.  [Selective nerve transfers to improve the control of myoelectrical arm prostheses]. , 2008, Handchirurgie, Mikrochirurgie, plastische Chirurgie : Organ der Deutschsprachigen Arbeitsgemeinschaft fur Handchirurgie : Organ der Deutschsprachigen Arbeitsgemeinschaft fur Mikrochirurgie der Peripheren Nerven und Gefasse : Organ der V....

[22]  Damjan Zazula,et al.  An approach to surface EMG decomposition based on higher-order cumulants , 2005, Comput. Methods Programs Biomed..

[23]  Dario Farina,et al.  Decoding the neural drive to muscles from the surface electromyogram , 2010, Clinical Neurophysiology.

[24]  D. Farina,et al.  Accessing the Neural Drive to Muscle and Translation to Neurorehabilitation Technologies , 2012, IEEE Reviews in Biomedical Engineering.

[25]  R. Merletti,et al.  Accuracy assessment of CKC high-density surface EMG decomposition in biceps femoris muscle , 2011, Journal of neural engineering.

[26]  Robert D. Lipschutz,et al.  The use of targeted muscle reinnervation for improved myoelectric prosthesis control in a bilateral shoulder disarticulation amputee , 2004, Prosthetics and orthotics international.

[27]  Damjan Zazula,et al.  Multichannel Blind Source Separation Using Convolution Kernel Compensation , 2007, IEEE Transactions on Signal Processing.

[28]  A. Willsky,et al.  Upper Extremity Limb Function Discrimination Using EMG Signal Analysis , 1983, IEEE Transactions on Biomedical Engineering.

[29]  Dario Farina,et al.  A signal-based approach for assessing the accuracy of high-density surface EMG decomposition , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[30]  D. Farina,et al.  Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric , 2014, Journal of neural engineering.

[31]  Damjan Zazula,et al.  Real-Time Motor Unit Identification From High-Density Surface EMG , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[32]  K. Englehart,et al.  Classification of the myoelectric signal using time-frequency based representations. , 1999, Medical engineering & physics.

[33]  B Hudgins,et al.  Myoelectric signal processing for control of powered limb prostheses. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[34]  Ping Zhou,et al.  Decoding a new neural machine interface for control of artificial limbs. , 2007, Journal of neurophysiology.

[35]  R N Scott,et al.  Myoelectric prostheses: state of the art. , 1988, Journal of medical engineering & technology.

[36]  Dario Farina,et al.  Unsupervised Bayesian Decomposition of Multiunit EMG Recordings Using Tabu Search , 2010, IEEE Transactions on Biomedical Engineering.

[37]  Robert D. Lipschutz,et al.  Targeted reinnervation for enhanced prosthetic arm function in a woman with a proximal amputation: a case study , 2007, The Lancet.