Internal Model Estimation for Closed-Loop Brain-Computer Interfaces

Monday, November 11, 2013 - 2:00pm
Fung Auditorium | Powell-Focht Bioengineering Hall
Byron Yu

Assistant Professor of Electrical & Computer Engineering and Biomedical Engineering

Carnegie Mellon University

Internal Model Estimation for Closed-Loop Brain-Computer Interfaces

The motor system successfully plans and executes sophisticated movements despite sensory feedback delays and effector dynamics that change over time.  Behavioral studies suggest that internal models are central to motor control, but neural correlates thereof have thus far been limited.  In the skeletomotor system, this problem isparticularly challenging due to the large number of neurons involved across multiple brain areas, non-linear limb dynamics, and multiple sensory feedback modalities.  In this talk, I will show how brain-computer interfaces (BCI), developed primarily to assist disabled patients, can be leveraged for basic scientific studies of motor control.  We consider an intracortical cursor-based BCI, which can be viewed as a simplified motor control system.  We found evidence that the subject uses an internal model during closed-loop BCI control and studied the timecourse of internal model adaptation during BCI learning.  We also developed a novel statistical algorithm that canextract an internal model from neural population activity recorded during BCI control.  This work suggests that closed-loop BCI experiments, combined with novel statistical analyses, can provide insight into the neural substrates of feedback motor control and motor learning.

Byron Yu received the B.S. degree in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 2001. He received the M.S. and Ph.D. degrees in Electrical Engineering in 2003 and 2007, respectively, from Stanford University. From 2007 to 2009, he was a postdoctoral fellow jointly in Electrical Engineeringand Neuroscience at Stanford University and at the Gatsby Computational Neuroscience Unit, University College London. He then joined the faculty of Carnegie Mellon University in 2010, where he is an Assistant Professor in Electrical & Computer Engineering and Biomedical Engineering.  He is broadly interested in how large populations of neurons process information, from encoding sensory stimuli to driving motor actions.  His group develops statistical algorithms for studying large-scale neural recordings (basic science) and for brain-computer interfaces (biomedical engineering).