Ultra-low power brain implants find meaningful signal in grey matter noise

By | July 28, 2020
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Cynthia Chestek, associate professor of biomedical engineering and core faculty at the Robotics Institute
Cynthia Chestek, associate professor of biomedical engineering and core faculty at the Robotics Institute (Image courtesy College of Engineering)

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—a discovery that could lead to long-lasting brain implants that can both treat neurological diseases and enable mind-controlled prosthetics and machines.

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