Long-term Robustness of Brain-Machine Interfaces

This research project deals with Brain-Machine Interfaces (BMIs), devices that use measurements of a subject's brain to provide a mechanism for them to interact with a computer. One of the main obstacles to the viability of current BMI approaches lies in the changing nature of the operating environment: the neural patterns associated with a desired behaviour can manifest differently under new recording conditions, or may even fundamentally vary over time due to reconfiguration of the neurons involved. These issues are prevalent across various BMI designs, even though the operating principles of those designs rely on different kinds of measurements or signal processing.

This project seeks to tackle this obstacle and improve the long-term robustness of BMIs through a variety of approaches: by developing novel digital signal processing techniques that better adapt to the variable operating environment, by improving our understanding of the underlying mechanics of neural variability to enable adaptive decoders to continuously calibrate themselves, and by investigating the nature of the feedback systems the brain itself requires to internally correct for this variability.

The project is inherently cross-disciplinary, and touches upon areas of Neuroscience and Biological Informatics, Control Engineering, Digital Signal Processing, and Machine Learning.