Six years ago, Ian Burkhart was an Ohio State University student on a beach vacation with his friends when a diving injury injured his spine, leaving him paralyzed in his arms and legs.
With a new "neural bypass" technology, Burkhart is able to use his hand for functional movements like pouring liquid out of a bottle, swiping a credit card, or playing a guitar video game in rhythm.
As reported in the journal Nature, it's the first time a neural-computer interface has given precise movement to a human limb using only the patient's thoughts.
The interface, developed by the laboratory group Batelle, is comprised of a chip implanted into Burkharts brain, which is connected via cable to a computer, then to a stimulator box that sends electronic signals to a sleeve on his wrist, which in turn stimulates his muscles.
Previously, researchers have used neural implants to have patients control a robotic arm, but in this case the neural prosthetic lets Burkhart regain partial use of his paralyzed limb — no bulky robotic attachment required.
Burkhart first had the chip — which reads the electric signals in his brain — implanted in April 2013. He's been coming into the lab at The Ohio State University Wexner Medical Center for two to three times a week since then.
"These new findings are the first demonstrations where it's now possible for the study participant to move individual fingers," says Chad Bouton, the lead technologist in the study.
"The problem that we started with is the fact that the brain can still generate all the neural activity around movement, but those signals try to descend the spinal chord, to the point where they encounter the injury, and they’re blocked for the most part," Bouton tells Tech Insider.
Bouton and his team developed software that could learn the brain patterns associated with specific movements, using an artificial intelligence technique called machine learning — similar to how Amazon learns what products to suggest, Netflix learns what movies to recommend, and how Google's self-driving car learns.
"That machine learning element actually improves itself every couple minutes," Bouton says. "Then the patient or user sees the improvement in his movement, and can then learn and improve those movements over time. The machine and the patient are learning together. After 10 to 15 minutes the performance goes up significantly."
Burkhart, the patient in the study, says that movement with the neural bypass feels surprisingly natural. He concentrates on making the movement and the machines take care of the rest. As of now, the technology doesn't provide any sensory feedback, though Bouton says he sees that coming in the future.
It would require FDA approval to take the device outside of the lab, but Burkhart says he gladly would if he could.
"If I could use that in my everyday life it would des crease the amount of assistance I need from other people," Burkhart says. "With the movements I can do today, I would take the system home in a heartbeat if they offered it."
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