The loss of bilateral hand function is a debilitating challenge for millions of individuals that suffered a motorcomplete spinal cord injury (SCI). We have recently demonstrated in eight tetraplegic individuals the presence of highly functional spared spinal motor neurons in the extrinsic muscles of the hand that are still capable of generating proportional flexion and extension signals. In this work, we hypothesized that an artificial intelligence (AI) system could automatically learn the spared electromyographic (EMG) patterns that encode the attempted movements of the paralyzed digits. We constrained the AI to continuously output the attempted movements in the form of a digital hand so that this signal could be used to control any assistive system (e.g., exoskeletons, electrical stimulation). We trained a convolutional neural network using data from 13 uninjured (control) participants and 8 motor-complete tetraplegic participants to study the latent space learned by the AI. Our model can automatically differentiate between eight different hand movements, including individual finger flexions, grasps, and pinches, achieving a mean accuracy of 98.3% within the SCI group. Moreover, the model could distinguish with 100% accuracy whether a participant had an injury or not, and it could also facilitate proportional control of certain movements after the injury. Analysis of the latent space of the model revealed that proportionally controllable movements exhibited an elliptical path, while movements lacking proportional control followed a chaotic trajectory. We found that proportional control of a movement can only be correctly estimated if the latent space embedding of the movement follows an elliptical path (correlation = 0.73; p < 0.001). These findings emphasize the reliability of the proposed system for closed-loop applications that require an accurate estimate of the spinal cord motor output.

The authors have declared no competing interest.

This work was supported by the Bavarian Ministry of Economic Affairs, Regional Development and Energy (StMWi) under Grant MV-2303-0006, by the European Research Council (ERC) through the project GRASPAGAIN under Grant 101118089, and by the German Federal Ministry of Education and Research (BMBF) through the project MYOREHAB under Grant 01DN23002 to ADV. The authors further gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universitat Erlangen-Nurnberg (FAU). The hardware is funded by the German Research Foundation (DFG).

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Ethics committee of the Friedrich-Alexander-Universitat (applications 22-138-Bm and 21-150-B) gave ethical approval for this work.

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