The motor unit (MU) represents the final common pathway consisting of the motor neuron and all the muscle fibres it innervates. Force is controlled by recruiting MUs or modulating their discharge rate. Traditional MU behaviour investigations use intramuscular electrodes and signal processing which identifies only a few MUs close to the electrode. Recently, High-Density surface electrodes(HDsEMG) allowed for MU identification non-invasively, but it has limitations. Decomposition algorithms (DA) can with validity and reliability identify MUs, but this is primarily in healthy young males. Greater adipose tissue thickness increases signal filtering, leading to a lower amplitude MU action potential which is more difficult to detect with the current DAs. This challenge has left a 'knowledge gap' in the study of MU behaviour in underrepresented populations (URP) (clinical, female, obese). This fellowship aims to optimize the state-of-the-art technology of HDsEMG and DA for a more diverse population. I aim to (1) identify limitations of HD-sEMG and DA in URPs; (2) adapt electrode configurations for URPs, and (3) optimize DA for individuals across URPs. This project is pertinent, as a large sector of the population is not studied, hindering MU behaviour research progression. I will focus on URPs where MU identification is challenging. I will fill this knowledge gap to allow the study of MU behaviour and assessment of all populations. Adapting this non-invasive technology is an important innovation which may be used as a model for diagnosis in clinical populations. The quality and success of this project are ensured by collaborations between myself and experts in signal processing (my supervisor Dr. Negro) and a company leading in multichannel electrode design. Their knowledge and expertise along with the facilities will guarantee the success of this project. The training will provide me with substantial hard and soft skills to become an independent translational scientist.

Neurorehabilitation technologies aim to promote functional neural plasticity in patients suffering from neuromuscular disabilities. Non-invasive systems for the magnetic or electrical stimulation of the neural pathways are particularly promising for interfacing directly with the nervous system of the patients and restoring coordinated movements. Despise these potentials, current neurorehabilitation systems have still significant limitations since they usually employ stimulation protocols based on specific assumptions on cortical and spinal connectivity. An efficient way to overcome this problem will be to estimate neural adaptations during the rehabilitation procedure and optimize its parameters directly. The project INcEPTION aims to develop innovative methods to estimate patterns of neural connectivity from the decomposition of high-density surface EMG signals and induce reorganization of the connectivity of motoneuron populations innervating the main arm and shoulder muscles using magnetic and electrical stimulation of cortical and sensory pathways. The revolutionary concept of the project will be to “implant” a signature of motoneuron correlation to promote changes in neural connectivity and, therefore, functional recovery of movements in chronic stroke individuals and breast cancer survivors. The approach will provide the possibility to better understand the mechanisms of neuroplasticity in the central nervous system and define efficient stimulation protocols to re-establish natural connectivity in the motoneuron pools of patient individuals. By combining multi-disciplinary contributions from the fields of neurorehabilitation, computational neuroscience, biomedical signal processing, and neurophysiology, the project INcEPTION aims to produce substantial progress toward a better understanding of the adaptation mechanisms involved in the connectivity of spinal motor neurons and its use in the next generation of neurorehabilitation systems.

This proposal addresses the automatic control of anaesthesia and the objective of developing an efficient and robust solution to increase patient safety and reduce post-operative complications. Automatic anaesthesia control is seen as one of the most important ways of achieving this goal. The control system should be able to provide significant benefits such as reducing the anaesthesiologist’s workload, limiting the influence of the human factor and minimizing the amount of drugs used. In this context, patient safety will be improved thus tackling both a severe public health problem and the significant economic burden on limited health resources. This project will use a novel event-based model predictive control approach for the anaesthesia process, addressing the key questions of efficient drug use, robustness regarding inter/intra-patient variability as well as considering its implementation and evaluation on real patients. The proposed approach has many advantages that can be useful in clinical practice such as adapting the actuation rate to the state of the patient, thanks to its predictive capabilities and event-based approach. The main objective is to develop new control approaches and optimize their performance in order to meet clinical requirements. Through the presented research, it will be possible to push the theoretical developments to the next stage, making them valuable milestones in the extensive implementation of automatic control techniques in the anaesthesia process. Moreover, an automatic control system can limit the influence of the human factor and provide a more unified solution for the anaesthesia process based on novel approaches. Activities developed under this proposal provide a unique opportunity to improve the quality of life of EU citizens and to reinforce the EU position as a central player in the global context through the high quality innovative multidisciplinary research.