High-Resolution Brain Machine Interfaces using Flexible Silicon Electronics
Right now, all of the tools that interface with our brains face a fundamental trade-off. We can either sample with low resolution, over large areas of the brain, or we can sample with fine resolution, over very small areas of the brain. This doesn’t fit with the way our brains are structured. With over 12 million neurons in each square cm of brain surface, we need to sample with high resolution over large areas in order to understand the way the brain works. The limitation is wiring. Every contact we put in the brain requires an individual wire and we can’t fit more than about 100 wires inside our heads. Using the same electronics that enable a digital camera to have millions of pixels without millions of wires, we can move some of the signal processing right to the sensors, allowing us to overcome the wiring bottleneck. The challenge is that traditional electronics are rigid and brittle. They are not compatible with the soft, curved surfaces of the brain. The solution is to make electronics that are flexible. Think of a piece of 2x4 lumber and a sheet of paper, they’re both made out of the same material, but have dramatically different physical properties. Leveraging that idea, we can make electronics that are extremely flexible, by making them very thin. Using these flexible electronics, I have developed high-density electrode arrays with thousands of electrodes that do not require thousands of external wires.
This technology has enabled extremely flexible arrays of 1,024 electrodes and soon, thousands of multiplexed and amplified sensors spaced as closely as 25 µm apart, which are connected using just a few wires. These devices yield an unprecedented level of spatial and temporal micro-electrocorticographic (µECoG) resolution for recording and stimulating distributed neural networks. I will present the development of this technology and data from in vivo recordings. I will also discuss how we are translating this technology for both research and human clinical use.
Jonathan Viventi is an Assistant Professor of Biomedical Engineering at Duke University. Dr. Viventi earned his Ph.D. in Bioengineering from the University of Pennsylvania and his M.Eng. and B.S.E. degrees in Electrical Engineering from Princeton University. Dr. Viventi's research applies innovations in flexible electronics, low power analog circuits, and machine learning to create new technology for interfacing with the brain at a much finer scale and with broader coverage than previously possible. He creates new tools for neuroscience research and technology to diagnose and treat neurological disorders, such as epilepsy. Using these tools, he collaborates with neuroscientists and clinicians to explore the fundamental properties of brain networks in both health and disease. His research program works closely with industry, including filing six patents and several licensing agreements. His work has been featured as cover articles in Science Translational Medicine and Nature Materials, and has also appeared in Nature Neuroscience, the Journal of Neurophysiology, and Brain. For these achievements, Dr. Viventi was selected to the 2014 MIT Technology Review “Top 35 Innovators Under 35” list, the 2014 Popular Science “Brilliant 10” list and an NSF CAREER Award.