Events Calendar

"Real-Time Brain-Machine Interface Architectures: Algorithmic Development and Experimental Implementation"

Speaker: Dr. Maryam Shanechi, Assistant Professor/Cornell University

Talk Title: "Real-Time Brain-Machine Interface Architectures: Algorithmic Development and Experimental Implementation"

Abstract: A brain-machine-interface (BMI) is a system that interacts with the brain either to allow the brain to control an external device or to control the brain's state. In this talk, I present my work on developing both these types of BMIs, specifically motor BMIs for restoring movement in paralyzed patients and a new BMI for control of the brain state under anesthesia. Motor BMI research has largely focused on the problem of restoring the original motor function by using standard signal processing techniques. However, devising novel algorithmic solutions that are tailored to the neural system can significantly improve the performance of these BMIs. Moreover, while building high-performance BMIs with the goal of matching the original motor function is indeed valuable, a compelling goal is that of designing BMIs that can enhance original motor function. Here, I first develop a novel BMI paradigm for restoration of natural motor function that incorporates an optimal feedback-control model of the brain and directly processes the spiking activity using point process modeling. I show that this paradigm significantly outperforms the state-of-the-art. I then introduce a BMI architecture aimed at enhancing original motor function that decodes all the elements of a sequential motor plan concurrently prior to movement. I demonstrate the successful implementation of both these designs in rhesus monkeys. I also present a motor BMI for control of the native limb that decodes neural activity from an alert subject to generate arm movements in a second temporarily paralyzed subject by stimulating its spinal cord. In addition to motor BMIs, I construct a new BMI that controls the state of the brain under anesthesia. This is done by designing stochastic controllers that infer the brain's anesthetic state from non-invasive observations of neural activity and control the real-time rate of drug administration to achieve a target brain state. I show the reliable performance of this BMI in rodent experiments.

Biography: Maryam M. Shanechi is an assistant professor in the School of Electrical and Computer Engineering at Cornell University. Her research focuses on using the principles of information and control theories and statistical signal processing to develop effective algorithmic solutions to basic and clinical neuroscience problems. Her work combines methodology development with in vivo implementation and testing. She received the B.A.Sc. degree with honors in Engineering Science from the University of Toronto in 2004 and the S.M. and Ph.D. degrees in Electrical Engineering and Computer Science (EECS) from the Massachusetts Institute of Technology (MIT) in 2006 and 2011, respectively. She has held postdoctoral fellowships at Harvard Medical School and in the EECS department at the University of California, Berkeley.

Host: Dr. Sandeep Gupta

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

Audiences: Everyone Is Invited

Contact: Mayumi Thrasher