Brain–machine interfaces (BMIs) are devices that can read brain activity and translate that activity to control an electronic device like a prosthetic arm or computer cursor. Many BMIs require invasive surgeries to implant electrodes into the brain in order to read neural activity. However, in 2021, Caltech researchers developed a way to read brain activity using functional ultrasound (fUS), a much less invasive technique.
Now, a new study is a proof-of-concept that fUS technology can be the basis for an “online” BMI—one that reads brain activity, deciphers its meaning with decoders programmed with machine learning, and consequently controls a computer that can accurately predict movement with very minimal delay time.
The study was conducted in the Caltech laboratories of Richard Andersen, James G. Boswell Professor of Neuroscience and director and leadership chair of the T&C Chen Brain–Machine Interface Center; and Mikhail Shapiro, Max Delbrück Professor of Chemical Engineering and Medical Engineering and Howard Hughes Medical Institute Investigator. The work was a collaboration with the laboratory of Mickael Tanter, director of physics for medicine at INSERM in Paris, France.