Development of closed-loop control systems for therapeutic applications of brain-computer interfaces

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The project will explore the design of personalised control algorithms for brain-computer interfaces used to treat neurological conditions in humans. The focus will be on feedback controllers based on known biomarkers associated with essential tremor and symptoms of Parkinson's disease using a technique known as deep brain stimulation. The goal will be to create controllers that are robust to parameter uncertainties and errors in the dynamic models linking electrical stimulation to the biomarkers. The control algorithms will also adapt to the variability in brain activity and structure between different patients, as well as the variations that occur for each individual over time due to disease progression. The use of targeted, rather than constant stimulation will lead to lower power consumption of these devices, as well as improvement in clinical outcomes for patients, particularly a reduction in adverse side effects. To achieve this goal, the project will consider the structure of dynamic models of the brain's response to stimulation, ensuring that they can accurately predict neural behaviour without incurring excessive complexity, which would limit the ability to fit model parameters to feasible amounts of data and prevent its use on low-power microcontrollers. Additionally, new techniques will be developed to identify the model parameters for each patient such that these can be used to design robust adaptive model predictive control algorithms. The project will also explore new scheduling algorithms to determine how and when the model updates will occur, based on estimates of the model's predictive performance as well as on estimates of the patients' state of activity that are known to affect the relevant biomarkers, such as walking, sleeping, or performing reaching tasks.Furthermore, the project will develop techniques for ensuring that parameter identification data is sufficiently rich, and for optimally trading off controller performance against the rate of accumulation of information about model parameters. This will improve accuracy and facilitate a reduction in the computational cost of calculating parameter updates, thereby improving the feasibility of deploying such control algorithms on embedded hardware. The control algorithms will be implemented on hardware emulators and tested in human patients in collaboration with Prof Huiling Tan's research group (Brain Network Dynamics Unit at the Nuffield Department of Clinical Neurosciences). This project falls within the EPSRC Control Engineering and Clinical Technologies research areas.