The intuitive control of upper - limb prostheses requires a man/machine interface that directly exploits biological signals. Here, we define and experimentally test an offline man/machine interface that takes advantage of the discharge timings of spinal moto r neurons. The motor - neuron behaviour is identified by deconvolution of the electrical activity of muscles reinnervated by nerves of a missing limb in patients with amputation at the shoulder or humeral level. We mapped the series of motor - neuron discharge s into control commands across multiple degrees of freedom via the offline application of direct proportional control, pattern recognition and musculoskeletal modelling. A series of experiments performed on six patients reveal that the man/machine interfac e has superior offline performance than conventional direct electromyographic control applied after targeted muscle innervation. The combination of surgical procedures, decoding and mapping into effective commands constitutes an interface with the output l ayers of the spinal cord circuitry that allows for the intuitive control of multiple degrees of freedom

Background Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. Methods We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. Results The regression approach using neural features outperformed regression on classic global EMG features (average R 2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). Conclusions These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control.

We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. this paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-eMG) decomposition to estimate muscle excitations. the muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. in addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. these results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function. State-of-the-art upper limb prostheses receive control commands from the user through a myoelectric interface. With this interface, the muscle fiber electrical activity in the residual limb is detected by recording surface electromyograms (sEMG), which are used to predict the user's motor intent and to control a prosthesis accordingly. Most devices use two electrode systems over an antagonist muscle pair to proportionally control one degree of freedom (DoF), e.g. hand open/close 1. Actuating more than one DoF requires a switching mechanism that leads to a slow, sequential, and unintuitive control of a maximum of two DoFs 2. This limited functionality in conjunction with the required extensive training leads to high abandonment rates of these prosthetic devices 3,4. To overcome this limitation and to provide natural control, classification methods have been proposed to estimate the movement of the wrist and hand from EMG features 5. Similarly, regression methods have been developed to continuously estimate kinematic variables (i.e. angular position or velocity of the joints), so that the user can actuate multiple DoFs concurrently 6,7. Both classification and regression approaches outperform conventional control methods in complex tasks, in terms of both speed and accuracy, providing a promising direction for advanced myoelectric control 8-11. Further improvement of regression methods might be facilitated by extracting the neural information embedded in the EMG. The neural drive to a muscle is the ensemble of discharges of its innervating motor neurons 12,13. Recent advances in the decomposition of the surface EMG allows for accurate decoding of the neural drive 14-16. Previously, we demonstrated that it is possible to extract neural information from high-density, multi-channel EMG used for myoelectric control in intact-bodied subjects and transradial amputees and in patients following targeted muscle reinnervation 17-19. The appl...

Targeted muscle reinnervation (TMR) is a surgical procedure used to redirect nerves originally controlling muscles of the amputated limb into remaining muscles above the amputation, to treat phantom limb pain and facilitate prosthetic control. While this procedure effectively establishes robust prosthetic control, there is little knowledge on the behavior and characteristics of the reinnervated motor units. In this study we compared the m. pectoralis of five TMR patients to nine able-bodied controls with respect to motor unit action potential (MUAP) characteristics. We recorded and decomposed high-density surface EMG signals into individual spike trains of motor unit action potentials. In the TMR patients the MUAP surface area normalized to the electrode grid surface (0.25 ± 0.17 and 0.81 ± 0.46, p < 0.001) and the MUAP duration (10.92 ± 3.89 ms and 14.03 ± 3.91 ms, p < 0.01) were smaller for the TMR group than for the controls. The mean MUAP amplitude (0.19 ± 0.11 mV and 0.14 ± 0.06 mV, p = 0.07) was not significantly different between the two groups. Finally, we observed that MUAP surface representation in TMR generally overlapped, and the surface occupied by motor units corresponding to only one motor task was on average smaller than 12% of the electrode surface. These results suggest that smaller MUAP surface areas in TMR patients do not necessarily facilitate prosthetic control due to a high degree of overlap between these areas, and a neural information—based control could lead to improved performance. Based on the results we also infer that the size of the motor units after reinnervation is influenced by the size of the innervating motor neuron.