Host Faculty: Professor Jon Viventi
Brain-machine interfaces (BMIs) translate neural activity into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, offering disabled patients greater interaction with the world. BMIs have recently demonstrated considerable promise in proof-of-concept animal experiments and in human clinical trials. However, a number of challenges for successful clinical translation remain, including system performance and robustness across time and behavioral contexts.
In this talk I will address these challenges by describing two classes of BMI experiments with rhesus monkeys. In these experiments we record from neurons in motor cortex using chronically implanted electrode arrays. The first class of experiments focus on control algorithm design. Through real-time closed-loop BMI experiments we demonstrate methods that increase performance and improve robustness. In the second class of experiments, we develop and verify a set of novel wireless neural recording systems, enabling the study of neural activity for longer time periods and across more complex behaviors. In addition to describing this work in animal model, I will introduce some of our recent work with human participants oriented towards the clinical translation of BMI.
Vikash Gilja is currently a research associate in the Neural Prosthetics Translational Laboratory at Stanford University, working with Krishna Shenoy, Ph.D., and Jaimie Henderson, M.D. He received the B.S. degree in Brain and Cognitive Sciences and the B.S./M.Eng. degrees in Electrical Engineering and Computer Science from MIT in 2003 and 2004. In 2010, he completed his Ph.D. in Computer Science at Stanford University with the thesis "Towards Clinically Viable Neural Prosthetic Systems."