Students Projects

Master Thesis: Analysis of Neural Input Dimensionality in Human Motor Control

The control of movement depends on the coordinated activity of many motor neurons. Recent advances in signal analysis have made it possible to identify patterns of shared activity between motor units, providing insights into how the nervous system organizes muscle control. In this project, you will develop and evaluate a new signal processing methodology to analyze the input received by motor units. The focus will be on building computational tools that can process high-resolution data.  [Contact us]

This work has applications in neuroscience, rehabilitation, and human–machine interfacing.

Your Qualifications

• Strong programming skills (e.g., MATLAB, Python, or similar).
• Strong background in signal processing and machine learning/deep learning methods.
• Interest in human motor control and neural signal analysis.
• Independent working style and motivation to solve interdisciplinary research questions.

Relevant Literature

• Del Vecchio, Alessandro, et al. "The forces generated by agonist muscles during isometric contractions arise from motor unit synergies." Journal of Neuroscience 43.16 (2023): 2860-2873.
• Hug, François, et al. "Correlation networks of spinal motor neurons that innervate lower limb muscles during a multi‐joint isometric task." The Journal of physiology 601.15 (2023): 3201-3219.
• Hug, François, et al. "Common synaptic input, synergies and size principle: Control of spinal motor neurons for movement generation." The Journal of physiology 601.1 (2023): 11-20.

Open applications

We welcome highly motivated students who wish to contribute but find that their interests are not reflected in our current projects. 

If you have an idea in mind, please reach out to us with your proposal. Always include your CV and academic transcripts to allow us to evaluate your application. [Contact us]