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

Abstract

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.

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

“…Grids of 64 electrodes for surface EMG recordings were located over each reinnervation point. The multi-channel EMG signals were processed to extract the series of discharges of the innervating motor neurons 24,40 . The identified spike trains were then used to define control commands.…”

“…Here we describe a neural interface that, following TMR 24,25 , extracts the sources of neural information -the discharge timings of motor neurons -through EMG deconvolution. The decoded neural information is then mapped into effective commands for intuitive prosthetic control.…”

A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings

“…26 However, recently, the use of EMG decomposition has been proposed for establishing neural interfaces in patients with limb amputations. 27 In these patients, nerve activity can be indirectly recorded by guiding nerves that previously innervated the missing limb muscles into accessary muscles that are used as biological amplifiers of nerve activity. 28 The resulting EMG signals are usually processed as global interference signals for extracting their amplitude as a crude estimate of nerve activity.…”

“…[29][30][31][32] However, recently, it has been proposed that the EMG signals from reinnervated muscles can be decomposed so that the efferent neural information of the innervating nerves can be extracted directly. 2,27 In this way, a neural interface is established which provides the same information as if the nerve would be directly interfaced with neural intrafascicular electrodes. This approach has been previously discussed as a theoretical possible way to control active prostheses 33 and is based on a strong evidence of direct association between the neural drive to muscles estimated from EMG decomposition and muscle force.…”

Prothetische Rekonstruktion der oberen Extremität

“…Unabhängig von der Signalextraktion wird die Verarbeitung der verschiedenen Muskelsignale mittels Mustererkennung eine noch intuitivere Prothesensteuerung ermöglichen, welche zusätzlich durch simple Feedbackmechanismen verfeinert werden wird [5,13].…”