Abstract:
Neural interfaces can restore or augment sensorimotor capabilities by converting high-bandwidth biological signals into control signals for an external device via a decoder algorithm. Users adapt their behavior in these interfaces via sensorimotor learning and decoder algorithms can adapt via machine learning. Leveraging user and decoder adaptation presents opportunities to improve usability and personalize devices. But we have limited understanding of how user and decoder learning interact. We also lack principled methods to model and optimize these complex two-learner dynamics. In this talk, I'll first present work suggesting that adaptive decoder algorithms influence brain learning in a motor brain-computer interface. I'll then present new computational methods based on control theory and game theory that allow us to analyze and generate predictions for user-decoder interactions, and experimental validation of these predictions in human myoelectric interface experiments.

Bio:
Dr. Orsborn is a Clare Boothe Luce Assistant Professor in the departments of Electrical & Computer Engineering and Bioengineering at the University of Washington. Her research explores sensorimotor plasticity in brain-computer interfaces and how plasticity is influenced by the algorithms used. She completed her Ph.D. at the UC Berkeley/UCSF Joint Graduate Program in Bioengineering and her postdoctoral training at NYU’s Center for Neural Science. Her work has been supported by a range of federal (NSF, NIH) and private (Simons Foundation) agencies, along with industry (Google, Meta).

Zoom:
https://umich.zoom.us/j/94337625486