Mafalda Ribeiro

ML and optimisation techniques for closed-loop neuromodulation of multiple nerve models

Project Summary

Neural interfaces are a promising field of research both from the perspective of uncovering the mechanisms behind neurological function at a cell cohort level, as well as for implantable devices used in the treatment of neurological conditions. Although some neuromodulation devices exist for clinical purposes, these devices conduct stimulation without feedback from the resultant effect on the nerve. My project therefore focuses on efficiently denoising, processing, and decoding neural recordings, which in turn can be used to inform electrical stimulation. This will involve collection of nerve activity data in multiple domains (in-vivo, ex-vivo, and simulated), as well as the development and analysis of different ML and optimisation algorithms to extract meaningful information from nerve data. Given the interdisciplinarity of this project, concepts of ethics, accountability, responsibility, and transparency are crucial, particularly when using ML alongside implantable, neuromodulation devices.


Doctoral Recognition Award Winner 2022

Research Interests

ML and bioelectronics for healthcare applications.


I received an MEng in Electrical and Electronic Engineering from the University of Bath, with a final year project focusing on the development of microfluidic lab-on-chip devices. I also had industrial placements at The Technology Partnership (TTP) and Intel UK focusing on medical device development and digital design and verification of application-specific ICs (ASICs) for mobile applications.


Dr Benjamin Metcalfe

Dr Michael Proulx

Dr Christof Lutteroth

Dr Paulo Rocha (University of Coimbra, Portugal)