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.
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.
Precise control of spinal motor neurons is crucial for voluntary hand and digit movements. However, the specific mechanisms by which motor unit ensembles govern dynamic synergistic and individual digit tasks remain poorly understood. We recorded synchronized 3D hand kinematics and high-density surface EMG (HD-sEMG) data from extrinsic hand muscles of twelve participants during 13 dynamic hand and digit movement tasks, consisting of single-digit flexion/extension and mechanically synergistic grasping tasks.We extracted single motor unit (MU) activity and identified identical MUs across tasks. We extracted 7.8 ± 1.8 MUs per task and participant and found 182 out of 554 total MUs active during multiple movements. Analysis of the MU discharge patterns revealed two groups of motor units that were categorized intoprime mover MUs, showing strong correlation between firing rate modulation and digit kinematics, andpostural MUswith little modulated activity. We found these motor units could switch between the two modes, showing either postural or movement encoding activation depending on the task. However, MUs acted as prime mover only for one specific digit. We further observed highly task specific recruitment ofprime moverMUs. Across participants, we found only 9 ± 8.2 % ofprime moverMUs active during a grasp task and any single digit task involved in the grasp motion.We draw three conclusions: (1) Single digits are controlled by distinct groups of MUs. (2) Unexpectedly, mechanically synergistic grasp movements are mostly controlled in a non-synergistic way by distinct groups of MUs. 3) Multiple manifolds construct the movement of the human hand, and each motor unit can flexibly switch between postural and dynamic modes.Significance StatementWe investigated the neural control of motor unit ensembles during single-digit and synergistic grasping tasks in dynamic conditions. We found that motor units exhibited strong movement-correlated activity only for one specific digit. We further observed highly task specific recruitment of motor units during mechanically synergistic grasp movements, showing that on a motor unit level, mechanically synergistic movements are controlled in a non-synergistic way. The findings extend the knowledge of motor unit recruitment strategies in natural movements and have strong implications in the field of neurorehabilitation and control of assistive devices.
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