Sales Ended

Multiscale Control-Theoretic Brain-Machine Interfaces

Event Information

Share this event

Date and Time

Location

Location

Imperial College London

Data Science Institute

William Penney Lab, South Kensington Campus

London, SW7 2AZ

View Map

Event description

Description

Biography

Maryam Shanechi is an assistant professor and the Viterbi Early Career Chair in Electrical Engineering at the University of Southern California (USC). Prior to joining USC, she was an assistant professor in the School of Electrical and Computer Engineering at Cornell University. She received the B.A.Sc. degree in Engineering Science from the University of Toronto in 2004 and the S.M. and Ph.D. degrees in Electrical Engineering and Computer Science from MIT in 2006 and 2011, respectively. She held postdoctoral positions at Harvard Medical School and at UC Berkeley from 2011-2013. She is the recipient of various awards including the NSF CAREER Award, the MIT Technology Review’s top 35 innovators under the age of 35 (TR35), the Popular Science Brilliant 10, and the ARO multidisciplinary university research initiative (MURI) award.


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. While for diverse applications, these two BMI classes can be viewed as stochastic closed-loop control systems. We work at the interface of control theory, statistical inference, and neuroscience to develop both classes of BMIs for various applications. In this talk, I present our work on motor BMIs for neural control of movement and on a new BMI for decoding the brain’s neuropsychiatric state and controlling it using electrical stimulation. Motor BMIs have largely decoded intended movement from a single scale of neural activity. However, new technology can record from the brain at multiple spatiotemporal scales. Moreover, modeling the brain processes underlying BMI control can help constrain and improve decoding algorithms. I first show our recent work on a motor BMI paradigm that enhances performance by incorporating an optimal feedback-control model of the brain and by directly processing the spiking activity using point process modeling. I then develop a multiscale modeling and decoding framework for BMIs to simultaneously infer information from small-scale spikes and large-scale local field potentials. In addition to motor BMIs, I show our work on formulating and developing a new BMI to treat depression using closed-loop electrical stimulation. I present a novel decoder that can predict mood variations over time from multisite intracranial human brain activity. I then develop a system-identification framework that uses a new electrical stimulation pattern to accurately model brain network dynamics in response to stimulation. Finally, building on these results, I construct a closed-loop BMI system for control of the neuropsychiatric brain state.

Share with friends

Date and Time

Location

Imperial College London

Data Science Institute

William Penney Lab, South Kensington Campus

London, SW7 2AZ

View Map

Save This Event

Event Saved