Lecturer: Alessandro Del Vecchio
Fields: AI, Neuroscience, BCI
Content
Spinal motor neurons represent the final gateway from neural intention to physical movement, making them crucial for any interface that aims to restore or augment motor function. In cases of spinal cord injury (SCI), paralysis of the hand muscles significantly impacts quality of life, as individuals lose the ability to perform fundamental tasks. However, our recent research demonstrates that even in individuals with motor complete SCI (C5–C6), the activity of spinal motor neurons remains accessible and task-modulatable. Using a minimally-invasive electromygraphic interface, we tested eight SCI individuals and identified distinct groups of motor units under voluntary control that could encode specific hand movements, from grasping to individual finger flexion and extension. By mapping these motor unit discharges to a virtual hand interface, we enabled participants to proportionally control multiple degrees of freedom, successfully matching various cued hand postures. These findings underscore the potential of wearable muscle sensors to access voluntarily controlled motor neurons in SCI populations, presenting a pathway to restore lost motor functions through assistive technologies.
Alongside this study, we explored the neural organization of motor unit activity in different muscle groups, focusing on the low-dimensional latent structures—or motor unit modes—that underlie the coordinated output of motor units. By applying factor analysis, we identified two primary motor unit modes that captured most of the variability in motor unit discharge rates across knee extensor and hand muscles. Interestingly, we observed a distinct pattern in the hand muscles, where motor unit modes were largely specific to individual muscles, whereas knee extensors displayed a more continuous distribution, with shared synaptic inputs leading to overlapping motor unit modes across muscle groups. Simulations with large populations of integrate-and-fire neurons confirmed the accuracy of these modes, shedding light on the common inputs that drive correlated activity in synergistic muscle groups.
Building on these insights, we have now developed an open-source software platform that translates real-time EMG activity into controllable movement outputs. This software seamlessly integrates with both exoskeletons and prosthetics, allowing for precise and intuitive movement control that aligns with the user’s intent. With this tool, we can now bring intuitive neuromotor interfaces closer to clinical reality, offering individuals with SCI and other neuromuscular impairments a new level of interaction and independence.
Literature
- Oliveira et al. Brain 2024 https://academic.oup.com/brain/article/147/10/3583/7631714
- Del Vecchio et al. J Neurosci 2023 https://pubmed.ncbi.nlm.nih.gov/36922028/
- Simpetru et al. arXiv 2024 https://arxiv.org/abs/2408.07817
Lecturer
Prof. Del Vecchio leads the n-squared lab (neuromuscular physiology and neural interfacing) at FAU since 2020 in the Dpt of AI in Biomedical Engineeirng. He is mainly interested in motor unit physiology, neuromotor interfaces, and machine learning.
Affiliation: FAU Erlangen-Nurnberg
Homepage: https://www.nsquared.tf.fau.de/