Theories on the neural control of movement are largely based on movement-sensing devices that capture the dynamics of predefined anatomical landmarks. Neuromuscular interfaces, such as surface electromyography (sEMG), can in theory surpass the limitations imposed by motion-based technologies by sensing the motor commands transmitted by the final pathway of movement, the motor units. The recording of motor unit activity may allow the prediction of the kinetics and kinematics continuously in time and space, without being limited to several biological and physical boundaries that digital cameras or inertial sensors suffer. However, current sEMG decoding algorithms can only predict few degrees of freedom (<3). By combining markerless machine vision and high-density sEMG electrodes, we aimed to test the hypothesis that a physiologically inspired deep neural network can reconstruct the movement of the human hand as precise as digital cameras and with the additional benefit of predicting the underlying forces (i.e., grasping a cup of coffee). We demonstrate that our deep learning model can continuously predict all degrees of freedom of the hand with negligible errors during natural motion tasks from 320 sEMG sensors placed only on the extrinsic hand muscles. Our deep learning model was able to display the 3D hand kinematics and the full force range of the hand digits during isometric contractions. The current results demonstrate that deep learning applied to EMG signals gives access to an unprecedented representation of the final neural code of movement.
Objective. High-density surface electromyography (HD-sEMG) allows the reliable identification of individual motor unit (MU) action potentials. Despite the accuracy in decomposition, there is a large variability in the number of identified MUs across individuals and exerted forces. Here we present a systematic investigation of the anatomical and neural factors that determine this variability. Approach. We investigated factors of influence on HD-sEMG decomposition, such as synchronization of MU discharges, distribution of MU territories, muscle-electrode distance (MED - subcutaneous adipose tissue thickness), maximum anatomical cross-sectional area (ACSAmax), and fiber CSA. For this purpose, we recorded HD-sEMG signals, ultrasound and, magnetic resonance images, and took a muscle biopsy from the biceps brachii muscle from 30 male participants drawn from two groups to ensure variability within the factors – untrained-controls (UT=14) and strength-trained individuals (ST=16). Participants performed isometric ramp contractions with elbow flexors (at 15, 35, 50 and 70% maximum voluntary torque - MVT). We assessed the correlation between the number of accurately detected MUs by HD-sEMG decomposition and each measured parameter, for each target force level. Multiple regression analysis was then applied. Main results. ST subjects showed lower MED (UT=5.1±1.4 mm; ST=3.8±0.8 mm) and a greater number of identified motor units (UT:21.3±10.2 vs ST:29.2±11.8 MUs/subject across all force levels). The entire cohort showed a negative correlation between MED and the number of identified MUs at low forces (r= -0.6, p=0.002 at 15%MVT). Moreover, the number of identified MUs was positively correlated to the distribution of MU territories (r=0.56, p=0.01) and ACSAmax (r=0.48, p=0.03) at 15%MVT. By accounting for all anatomical parameters, we were able to partly predict the number of decomposed MUs at low but not at high forces. Significance. Our results confirmed the influence of subcutaneous tissue on the quality of HD-sEMG signals and demonstrated that MU spatial distribution and ACSAmax are also relevant parameters of influence for current decomposition algorithms.
The paralysis of the muscles controlling the hand dramatically limits the quality of life of individuals living with spinal cord injury (SCI). Here, we present a non-invasive neural interface technology that will change the lives of individuals living with cervical SCI (C4-C6). We demonstrate that eight motor- and sensory-complete SCI individuals (C5-C6, n = 7; C4, n = 1) are still able to task-modulate in real-time the activity of populations of spinal motor neurons with spared corticospinal pathways. In all tested patients, we identified groups of motor units under voluntary control that encoded a variety of hand movements. The motor unit discharges were mapped into more than 10 degrees of freedom, ranging from grasping to individual hand digit flexions and extensions. We then mapped the neural dynamics into a real-time controlled virtual hand. The patients were able to match the cue hand posture by proportionally controlling four degrees of freedom (opening and closing the hand and index flexion/extension). These results demonstrate that wearable muscle sensors provide access to voluntarily controlled neural activity in complete cervical SCI individuals.
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