Drastically reducing the power and computation needed to identify our intentions, researchers open up a future of advanced therapies and machines enabled by our thoughts.
July 27, 2020

By tuning into a subset of brain waves, University of Michigan researchers have dramatically reduced the power requirements of neural interfaces while improving their accuracy—a discovery that could lead to long-lasting brain implants that can both treat neurological diseases and enable mind-controlled prosthetics and machines.

The team, led by Cynthia Chestek, associate professor of biomedical engineering and core faculty at the Robotics Institute, estimated a 90% drop in power consumption of neural interfaces by utilizing their approach.

“This is a big leap forward,” Chestek said. “To get the high bandwidth signals we currently need for brain machine interfaces out wirelessly would be completely impossible given the power supplies of existing pacemaker-style devices.”