Abstract: The field of motor brain-computer interfaces (BCIs) has advanced dramatically. Our ability to accurately decode neural activity to directly control a cursor, robotic arm, or the patient’s own muscles continues to improve. However, this control remains robotic and limited compared with natural human performance. Most BCI decoding relies on each neuron having a fixed and linear relationship to a given set of degrees of freedom. In experimental results from a reach-to-grasp task, Dr.Rouse will describe the sequential phases of movement observed with EMG, kinematic, and single-unit neurophysiologic recordings. He also will show the broad tuning throughout the entire upper forelimb region of primary motor cortex to both reach location and grasp object type and how it transitions between phases of the movement. Dr. Rouse will demonstrate why this sequential, selective tuning can serve as an important principle for BCI design. By using active dimension selection and four ethologically relevant dimensions of control, he will show how a simple 16 single unit BCI can efficiently control a virtual hand to achieve eight different postures with 93 percent accuracy, with average movement times of ~1 second. By analyzing large-dimensional datasets of joint kinematics, EMG, and neural activity, he focuses on understanding how neural populations can generate motor output across a broad dynamic range with speed and precision.
Biography: Adam G. Rouse is currently a research assistant professor at the University of Rochester in the Department of Neuroscience. Previously he completed a B.S. in Biomedical Engineering from Washington University in St. Louis and then remained there to complete an M.D. and Ph.D. in Biomedical Engineering. Dr.Rouse's thesis work in Dr. Daniel Moran’s lab examined the signal properties and neural adaptation with chronic brain-computer interfaces using electrocorticography (ECoG). Since moving to Rochester, he has been a member of the Finger Movement Laboratory directed by Dr. Marc Schieber studying the neural control of complex movements such as reaching and grasping as well as developing brain-machine interfaces for dexterous hand control.