Prof. Dante Muratore of TU-Delft

Brain-computer interfaces (BCIs) of the future will be used to treat neurological disorders for which a cure does not exist yet and expand on the interaction between humans and machines. For this futuristic view to become a reality, we must overcome many technological challenges. A major goal is to increase the number of neurons that we can simultaneously interact with. To do so, future systems will record from more than a thousand channels at the same time. These systems will create a massive amount of raw data that needs to be compressed on the implant to lower the power consumption of the wireless link. As custom signal specifications for BCI applications become clearer and are diverging from those needed for basic neuroscience, there is an opportunity for application-specific lossy compression that further improves the power efficiency of these implants. In this talk, I will cover the basic aspects of compression for BCIs and describe two different compression schemes for a motor BCI and an artificial retina.

Biography:

Dante Gabriel Muratore is an assistant professor of Bioelectronics at the Delft University of Technology. He received the B.S. degree and the M.S. degree in Electrical Engineering from Politecnico of Turin in 2012 and 2013. He received his Ph.D. degree in Microelectronics from the University of Pavia in 2017. From 2015 to 2016, he was a Visiting Scholar at MTL labs at the Massachusetts Institute of Technology. From 2016 to 2020, he was a Postdoctoral Fellow at Stanford University. He is the recipient of the Wu Tsai Neurosciences Institute Interdisciplinary Scholar Award. His research focuses on hardware-algorithm co-design for brain-computer interfaces, bioelectronics, sensor interfaces, and machine learning at the edge.