I know this has been said countless times, but hear me out… I’m genuinely fascinated by how our central and peripheral nervous systems generate movement. In my PhD, I explore ways to decode motor intentions from EMG signals (electrical recordings of skeletal muscle activity) to create more natural, intuitive, and energy-efficient control commands for myoelectric interfaces.
My work focuses on spike-based processing, inspired by the way information is transmitted in the brain, and integrates the now-popular spiking neural networks (SNNs) to develop more accurate and responsive motor control, with the goal of efficiently running these models on neuromorphic chips (hopefully?).
What excites me the most? Experimenting with human subjects! I love designing experiments to test new ideas for improving EMG-based movement decoding, running real-time tests with participants (especially potential prosthesis users!), gathering feedback, and iterating to refine the system even further.
ZNZ Symposium 2023 - Controlling Prosthesis with Motor Units
ISEK 2024 - Decoding intraneural recordings using SNN
EMBC 2024 - A priori channel selection for finger force regression
