Seminar: 11/17 - Jose Carmena
11:00 to 12:30 PM at:
Program in Cognitive Science and Department of Electrical Engineering & Computer Science
The advent of multi-electrode recordings and brain-machine interfaces (BMIs) has provided a powerful tool for the development of neuroprosthetic systems. BMIs are powerful tools that use brain-derived signals to control artificial devices such as computer cursors and robots. By recording the electrical activity of hundreds of neurons from multiple cortical areas in subjects performing motor tasks we can study the spatio-temporal patterns of neural activity and quantify the neurophysiological changes occurring in cortical networks, both in manual and brain control modes of operation. In previous work at Duke University we demonstrated that monkeys can learn to reach and grasp virtual objects by controlling a robot arm through a BMI using visual feedback, even in the absence of overt arm movements. Learning to operate the BMI is paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMI are incorporated into motor and sensory cortical representations. While significant breakthroughs have been achieved in recent years and the field is rapidly taking off, there are challenges that need to be met before BMI technology fully reaches the clinical realm. In this talk I will outline the emerging directions the field is taking towards the development of neuroprosthetic devices for the impaired.
"Emerging Directions in Brain-Machine Interfaces"