Students Projects

Important! When applying to any project, please always include your CV and academic transcript to allow us to evaluate your application.

Master’s Thesis: Unsupervised Deep Learning Approaches for Generalizable Myoelectric Control

Current myoelectric control strategies often require extensive supervised training data and show limited generalization across users. In this Master’s thesis, you will explore unsupervised deep learning approaches to improve gesture estimation from muscle activity without relying heavily on labeled data. Using surface electromyography (sEMG) recordings, you will develop and evaluate models capable of learning robust, transferable representations of muscle activation patterns. The ultimate goal is to enable more adaptive and user-independent control of myoelectric interfaces.

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Your Tasks

• Review the state of the art in self-supervised and unsupervised learning for biosignal decoding
• Develop deep learning architectures for feature extraction from unlabeled sEMG data
• Integrate learned representations into gesture classification or regression frameworks
• Evaluate model generalization across participants and recording sessions

Your Qualifications

• Proficiency in Python
• Strong background in deep learning (preferably experience with LLMs, transformers, or time-series modeling)
• Preferably, knowledge in the processing of sEMG signals
• Strong interest in neural interfaces, representation learning, and human-machine interaction

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.

Exploring neural content and spatial-temporal information from high density surface electromyographic sensors

This master thesis focuses on extracting spatial-temporal features from high-density surface electromyographic (HD-sEMG) signals to decode motor intention, enabling more precise and dexterous human-machine interfacing. The work involves analyzing signal features to achieve accurate classification/regression of different movements, which can then be applied to control bionic limbs. Studies investigating motor modularity in humans and motoneuron activity are also highly encouraged, as they can provide insights into optimizing control strategies for assistive devices. [Contact us]

Your Tasks

• Review the state of the art in high-density EMG signal processing and neural decoding
• Analyze spatio-temporal signal features from HD-sEMG open datasets
• Implement and propose new methods to map neural information to movement intent
• Investigate modularity in motor control and adaptation

Your Qualifications

• Enrolled in a Master’s program (STEM, computer science, neuroscience, biomedical engineering, or related field)
• Background in signal processing and/or machine learning

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]