Title: Brain Machine Interfaces: The Need for Better Science

Abstract: Nearly 6 million people in the US are affected by some form of paralysis.  For such patients, whose treatment options remain limited, neural prosthetic systems using a brain machine interface (BMI) hold the potential to drastically improve their quality of life by enabling them to use a robotic arm or to control a computer.  Recent research has demonstrated the promising clinical potential of neural prosthetics, but the performance and robustness of these systems remains limited.  This talk will focus on the functionality and limitations of existing neural prosthetic systems, and our current efforts, under Obama’s BRAIN initiative, to develop novel tools to deepen our understanding of the neural basis of movement generation, on which BMI systems operate.  Such systems typically operate by recording activity patterns of neurons in the motor cortex using an implanted microelectrode array.  These sensors are limited to recording from roughly 20-100 neurons, sampling less than .001% of the neurons under the array.  To address these limitations, we are developing a novel two-photon calcium imaging system for in-vivo optical recording at cellular resolution in awake and behaving primates.  If successful, this technology holds the potential to enable recording from hundreds or thousands of neurons simultaneously, enabling us to ask new scientific questions to improve the performance of neural prosthetic systems.

Bio: Eric Trautmann is currently a PhD candidate in neurosciences at Stanford University in the Neural Prosthetic Systems Lab led by Dr. Krishna Shenoy.  He obtained his Bachelors and Masters from Thayer in 2008 and 2009, working to design and build the autonomous Yeti robot for surveying antarctic ice sheets as part of his 89/90 project and subsequent MS research.  Upon graduation, Eric worked for two years at Physical Sciences Incorporated designing quad-rotor helicopter drones and developing sensor fusion algorithms for applications in defense.  He began his PhD at Stanford in 2011, applying his background in electrical engineering, mechatronics, and machine learning to develop novel tools and analysis methods for understanding population dynamics of neural activity, focusing on the primate motor system.