Brain-machine interfaces (BMI) aim to restore movement in millions of disabled people. Despite successful laboratory demonstrations, the lack of generalizability and robustness, and the low performance remain key challenges hindering clinical viability. BMIs should be able to control a variety of prosthetic devices, and to exploit any neural signal modality as their control signal. This research develops generalizable, robust, and closed-loop BMI architectures for neuroprosthetic control, and applies these architectures both to build proficient neuroprosthetics and to investigate the brain mechanisms underlying such control. This research is fully integrated with outreach and education activities including interactions with disabled Veterans, and mentoring of women and underrepresented minorities.
One main reason for the lack of generalizability and robustness in existing BMIs is that they do not model the behavior of the single common component in all BMI settings, i.e., the brain, which controls the movement. Moreover, performance of current BMIs has been sacrificed because they have not been adapted to the statistical properties of the recorded neural signal modality and have used standard signal processing algorithms for any modality. This research develops a BMI that can control prosthetics with various dynamics and using different neural recording modalities. It builds a novel model of the brain in closed-loop control, constructs principled stochastic models for different recording modalities, and combines these two models to devise an adaptive supervised learning and decoding algorithm. It also uses the architecture to investigate the brain mechanisms underlying neuroprosthetic control. This research enables a universal and principled neuroprosthetic architecture, replacing ad-hoc approaches; it significantly improves neuroprosthetic performance; finally, it allows for a deeper understanding of the fundamental brain mechanisms underlying neurprosthetic and motor control.
