Implantable and wearable devices are increasingly being applied to the treatment of new diseases each year. In particular, neurological disorders such as epilepsy, migraine, and Alzheimer’s disease are of major public health concern around the world, necessitating the need to explore more efficient therapies. Despite significant advances in neural interface systems, the small number of recording channels in existing technology remains a barrier to their therapeutic potential. This is mainly due to the fact that simultaneous recording from large number of electrodes imposes stringent energy and area constraints on the integrated circuits that interface with these electrodes. In this talk, I will first discuss an efficient compressive sensing framework for multichannel cortical implants. Next, I will present the design of our sub-microwatt per channel closed-loop seizure control device and both its in-vivo and offline performance. I will then briefly discuss our latest work on the integration of machine learning algorithms for on-chip classification of neural data. Finally, I will give examples of how these results may be used towards designing new devices, to enhance the lives of millions of people suffering from other disabling conditions in future.
Mahsa Shoaran is currently a postdoctoral fellow in Electrical Engineering and Medical Engineering at the California Institute of Technology. She received her PhD in Electrical Engineering from EPFL, Switzerland, in 2015, and her B.Sc. from Sharif University of Technology. Her research interest broadly includes innovative circuit and system design for diagnostic and therapeutic applications. She is a recipient of both early and advanced Swiss National Science Foundation Postdoctoral Fellowships, and the NSF Award for Young Professionals Contributing to Smart & Connected Health. Mahsa was named a Rising Star in EECS by MIT in 2015.