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closed-loop_brain-machine_interface_architectures [2016/09/01 19:15] (current)
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|+||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 these two BMI types are for different applications, from a system-theoretic standpoint, they can both be viewed as closed-loop control systems. Our group develops BMI architectures by working at the interface of systems theory, statistical signal processing and neuroscience. In this talk, I present our work on designing both these BMIs, specifically motor BMIs for restoring movement in paralyzed patients and a BMI for control of the brain state under anesthesia. I also show ongoing work on a completely new BMI for treatment of neuropsychiatric disorders using closed-loop control of electrical stimulation to the brain.|
|+||Motor BMIs have largely used standard signal processing techniques. However, devising novel algorithmic solutions that are tailored to the neural system can significantly improve BMI performance. Here, I develop a novel BMI paradigm for movement restoration 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 in closed-loop monkey experiments. Additionally, 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. Finally I present ongoing work on BMIs for closed-loop electrical stimulation of the brain to treat neuropsychiatric disorders such as depression.|
|+||Bio: Maryam Shanechi is an assistant professor in the Ming Hsieh Department of 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 has been named by the MIT Technology Review as one of the world’s top 35 innovators under the age of 35 for her pioneering work on brain-machine interfaces.|