(Credit: Sonya Todorova)
Broadly speaking, my laboratory investigates how sensory feedback impacts the neural representation of motor intent. One of the major tools we use is the brain-computer interface (BCI). These devices allow us to tap into the output of a network of neurons and use that recorded activity to directly drive a device, like a computer cursor. By creating a defined link between neural activity and behavior, BCIs provide a unique window for probing brain processes that would otherwise remain covert, like learning, adaptation, and the dynamic evolution of the intent signal. Ultimately a basic understanding of these phenomena will not only inform us about the fundamental limits of motor control processes, but will also help propel the development of new neural prosthetic devices. My research has two main thrusts. First, I develop novel computational and experimental techniques that leverage BCIs as a research tool for investigating the neural mechanisms of sensorimotor adaptation and skill acquisition. Second, I design new BCI decoding algorithms to enhance the performance of these devices and hasten their clinical translation.