EECS Department Colloquium Series
Real-Time Brain-Machine Interface Architectures: Algorithmic Development and Experimental Implementation
Wednesday, January 22, 2014
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 and then introduce a BMI architecture aimed at enhancing original motor function. I demonstrate the successful implementation of both these designs in rhesus monkeys. In addition to motor BMIs, I construct a new BMI that controls the state of the brain under anesthesia and show its reliable performance in rodent experiments.
For restoration of lost motor function, I develop a new BMI paradigm that incorporates an optimal feedback-control model of the brain and directly processes the spiking activity using point process modeling. I first apply this paradigm to construct a two-stage decoder to decode jointly the target and trajectory of a reaching movement. I then extend this paradigm by incorporating adaptive point process filtering and show that it significantly outperforms the state-of-the-art. For enhancement of original motor function, I introduce a concurrent BMI architecture for performing a sequence of planned movements. In contrast to a traditional BMI, this BMI decodes all the elements of the sequential motor plan concurrently prior to movement, which enables it to find ways to perform the task more effectively. Finally, I formulate a different type of BMI for control of the brain state under anesthesia. I design 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.
Maryam Shanechi is an assistant professor in the School of Electrical and Computer Engineering at Cornell University. 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. In her S.M. work, she developed efficient coding architectures for communication over multiple-input multiple-output channels. In her Ph.D. work, she led an interdisciplinary project across three laboratories at MIT, Harvard Medical School (HMS), and Massachusetts General Hospital (MGH), to develop novel brain-machine interface architectures. She has held postdoctoral fellowships at HMS/MGH and in the EECS department at UC Berkeley. 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.
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