Non-Invasive Neural Interfacing for Tetraplegic Individuals Using Residual Motor Neuron Activity Decoded at the Forearm or Wrist

Hand paralysis due to spinal cord injury (SCI) greatly limits the quality of life of injured individuals. Despite complete loss of hand digit control, however, residual electrical muscle activity is often detected from these injured individuals. From this activity, individual motor unit action potentials can be identified and potentially used to infer their motion intent for interfacing purposes. We recently demonstrated that residual motor units can be decoded from tetraplegic individuals with SCI, by mapping both proximal and distal forearm activity using hundreds of electromyography (EMG) electrodes. Yet, few explored the feasibility of neural interfacing using only forearm motor units or even far-field wrist motor units in SCI, which will facilitate the use of fully wearable systems such as EMG bracelets. Here, we recognize finger gestures in eight tetraplegic individuals (Seven with motor complete SCI and one with motor incomplete SCI), using either forearm or wrist motor units. We demonstrate that motion-wise surface EMG decomposition can effectively increase the number of decomposed motor units from both forearm and wrist (on average 41.25 $\pm$ 24.14 from the forearm and 30 $\pm$ 9.72 from the wrist) and to reach high accuracy in gesture recognition at both locations (82% to 100% with the forearm data, and 62% to 99 with the wrist data). The decomposition met the requirement of real-time implementation. Moreover, the correlation between far-field motor units activity recorded from the wrist with the activity recorded at the forearm is revealed, further suggesting both locations are suitable for interfacing.