MAR 16, 2017 06:00 AM PDT
Decoding the Mind using Brain Machine Interfaces: Turning Thought into Action
Presented at the Neuroscience 2017 Virtual Event
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  • Professor, Department of Organismal Biology and Anatomy, Chair, Committee on Computational, Neuroscience, Univeristy of Chicago
      Nicholas G. "Nicho" Hatsopoulos, Ph.D. is currently a Professor at the University of Chicago. Dr. Hatsopoulos was also Chairman of the Computational Neuroscience graduate program from 2008-2015. He is currently running a laboratory with two graduate students, five postdoctoral fellows, and several technicians which is funded in part by the National Institutes of Health. From January 1998 to December 2001, Dr. Hatsopoulos was an Assistant Professor of Research in the Department of Neuroscience at Brown University. Dr. Hatsopoulos completed two postdoctoral research fellowships, one in the Department of Neuroscience at Brown University and the other in the Computational Neuroscience Program at the California Institute of Technology.
      Dr. Hatsopoulos completed his B.A. in Physics from Williams College in 1984, his M.S. in Psychology in 1991 and his Ph.D. in Cognitive Science from Brown University in 1992.
      In 2001, he co-founded a company, Cyberkinetics Neurotechnology Systems, which took the basic scientific research he and his colleagues conducted to develop neural prosthesis technology to assist people with severe motor disabilities.
      His research focuses on the neural basis of motor control and learning. He is investigating what features of motor behavior are encoded and how this information is represented in the collective activity of neuronal ensembles in the motor cortex. He is also interested in how these representations change as motor learning occurs. To answer these questions, the electrical discharge of many motor cortical neurons is simultaneously recorded using multi-electrode arrays and correlated with motor behavior. The encoding properties of individual motor cortical neurons are being studied to determine how these single cell properties relate to higher-order representations involving groups of neurons. The possibility that changes in functional connectivity among neurons may occur during motor learning is also being explored.


    A fundamental challenge in developing brain machine interfaces (BMIs) is building a decoder between patterns of brain activity and movement in patients with spinal cord injury, ALS, and amputation because these patients cannot move their intact limb .   We demonstrate two approaches to solving this problem.  First, we show that visual observation of action can automatically trigger mirror-like responses in primary motor cortex that are similar to the responses that occur during action.  Using these mirror-like responses, we show that brain activity can be mapped to movement without overt arm movement.  Second, we demonstrate that amputees can learn an arbitrary mapping between brain activity and movement of a robot after long-term exposure to a BMI.  Moreover, we also show how the functional connectivity among neurons controlling the robot undergo modifications during BMI learning which may provide an interesting model of motor skill acquisition.


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