A direct spinal cord–computer interface enables the control of the paralysed hand in spinal cord injury

Souza de Oliveira D, Ponfick M, Braun D, Oßwald M, Sierotowicz M, Chatterjee S, Weber DJ, Eskofier B, Castellini C, Farina D, Kinfe TM, Del Vecchio A (2024)

Publication Language: English

Publication Status: Accepted

Publication Type: Journal article, Original article

Future Publication Type: Journal article

Publication year: 2024

Journal

Brain Oxford University Press

Publisher: Oxford Academic Press

Book Volume: 147

Pages Range: 3583-3595

Journal Issue: 10

URI: https://academic.oup.com/brain/advance-article/doi/10.1093/brain/awae088/7631714

DOI: 10.1093/brain/awae088

Open Access Link: https://academic.oup.com/brain/article/147/10/3583/7631714

Abstract

The paralysis of the muscles controlling the hand dramatically limits the quality of life of individuals living with spinal cord injury (SCI). Here, with a non-invasive neural interface, we demonstrate that eight motor complete SCI individuals (C5-C6) are still able to task-modulate in real-time the activity of populations of spinal motor neurons with residual neural pathways.
In all SCI participants tested, we identified groups of motor units under voluntary control that encoded various hand movements. The motor unit discharges were mapped into more than 10 degrees of freedom, ranging from grasping to individual hand-digit flexion and extension. We then mapped the neural dynamics into a real-time controlled virtual hand. The SCI participants 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 spared motor neurons that are fully under voluntary control in complete cervical SCI individuals. This non-invasive neural interface allows the investigation of motor neuron changes after the injury and has the potential to promote movement restoration when integrated with assistive devices.

Authors with CRIS profile

Daniela Souza de Oliveira Department Artificial Intelligence in Biomedical Engineering (AIBE) Dominik Braun Juniorprofessur für Neuromuscular Physiology and Neural Interfacing Marius Oßwald Juniorprofessur für Neuromuscular Physiology and Neural Interfacing Marek Sierotowicz Assistive Intelligent Robotics Lab Björn Eskofier Machine Learning and Data Analytics Lab Claudio Castellini Assistive Intelligent Robotics Lab Thomas Mehari Kinfe Professur für Neurochirurgie mit dem Schwerpunkt Funktionelle Stereotaktische Neurochirurgie Alessandro Del Vecchio Juniorprofessur für Neuromuscular Physiology and Neural Interfacing

Involved external institutions

Sana Krankenhaus Rummelsberg DE Germany (DE) Carnegie Mellon University US United States (USA) (US) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Souza de Oliveira, D., Ponfick, M., Braun, D., Oßwald, M., Sierotowicz, M., Chatterjee, S.,... Del Vecchio, A. (2024). A direct spinal cord–computer interface enables the control of the paralysed hand in spinal cord injury. Brain, 147(10), 3583-3595. https://doi.org/10.1093/brain/awae088

MLA:

Souza de Oliveira, Daniela, et al. "A direct spinal cord–computer interface enables the control of the paralysed hand in spinal cord injury." Brain 147.10 (2024): 3583-3595.

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