Brain interface pioneers find meaningful signal in the grey matter noise

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.

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. This discovery 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.

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