Professor of Electrical Engineering, Cognitive Science and Neuroscience
Research Areas: controls, sensorimotor learning and control, brain-machine interfaces, neuroprosthetics, neural ensemble computation
Our research is concerned with investigating the neural basis of sensorimotor learning and control, and how we can use this knowledge in a brain-machine interface (BMI) context to improve the quality of life for the neurologically impaired.
BMI is about transforming thought into action, or conversely, sensation into perception. This novel paradigm contends that a user can perceive sensory information and enact voluntary motor actions through a direct interface between the brain and a prosthetic device in virtually the same way that we see, walk or grab an object with our own natural limbs. Proficient control of the prosthetic device relies on the volitional modulation of neural ensemble activity, achieved through training with any combination of visual, tactile, or auditory feedback.
BMI is also a powerful tool for modern systems neuroscience to study learning and adaptation in the brain. It allows visualization of neural circuit function through spatiotemporal patterns of neural activity while subjects perform behavioral tasks in both manual and brain control modes of operation.
At the BMI Systems Lab we use electrophysiological, behavioral and computational techniques to ask scientific questions about how the brain controls movement, as well as to achieve the technological milestones required to bring BMI to the clinical realm.