Title: Electronic Brain-Machine Interfaces for Sensory Encoding
Abstract: Communicating with brains directly is among the most thrilling advancements in our era. Recent brain-machine interface researches have made substantial progress at acquiring neural signals and decoding brain activities. However, how to send signals back to the brains, e.g. encoding sensation and perception, remains a significant challenge. In this talk, I will present my research on novel electronic brain-machine interface design for sensory encoding. Specifically, I will describe the design of an intelligent brain-machine interface system that enables closed-loop neuromodulation in freely behaving animals. Novel integrated circuits and algorithm co-design techniques will be discussed. Through close collaboration with neuroscientists, I have successfully demonstrated the first chronic brainstem neuroprosthetic for continuous sensory restoration in non-human primates. This technology holds great promise in restoring the sensory communication of patients suffering from paralysis, spinal cord injuries, various brain lesions and degenerative conditions. In the end, I will discuss my future research directions with a focus on addressing the barriers in translating the technologies into clinical practices.
Bio: Dr. Xilin Liu is currently a Staff Engineer at Qualcomm. He received his Ph.D. degree from the University of Pennsylvania. His research interests include integrated circuits, algorithms and system design for emerging applications, especially brain-machine interfaces. He received the 2020 Qualcomm Outstanding Contribution Award and the 2015-16 IEEE Solid-State Circuits Society (SSCS) Predoctoral Achievement Award. His publications have been recognized as the Best Student Paper Award on the 2017 International Symposium on Circuits and Systems (ISCAS), the Best Paper Award (1st place) on the 2015 Biomedical Circuits and Systems Conference (BioCAS), and the Best Paper Award of the biomedical track on the 2014 ISCAS. He was also the recipient of the Student-Research Preview Award on the 2014 IEEE International Solid-State Circuits Conference (ISSCC).