Combined with a computer with artificial intelligence and the aforementioned robotic arm, a University of Minnesota neuronal chip can read and interpret brain signals, allowing individuals with amputated limbs to control the arm using only their thoughts. Credit: Neuroelectronics Laboratory, University of Minnesota

Researchers at the University of Minnesota-Twin have developed a more accurate, less invasive technology that allows people with amputations to move robotic arms using brain signals instead of muscles.

Many modern commercial limb prostheses use a system of rope and harness that is controlled by the shoulders or chest, and more advanced limbs use sensors to capture subtle muscle movements in the patient’s existing limb above the device. But both options can be cumbersome, unintuitive, and take months of practice for people with amputations to learn to move.

Researchers from the University’s Department of Biomedical Engineering with the help of industry staff have created a small implantable device that attaches to a peripheral device. nerve in the hand of man. In combination with en artificial intelligence computer and robotic arm, the device can read and interpret brain signals, allowing individuals with amputated upper limbs to control the arm using only their thoughts.

The latest work by researchers is published in Journal of Neural Engineeringpeer-reviewed scientific journal for the interdisciplinary field of neural engineering.

“It’s much more intuitive than any commercial system,” said Jules Ann Tuan Nguyen, a doctoral student at the University of Minnesota’s twin cities. biomedical engineering Ph.D. graduate. “With other commercial prosthetic systems, when people with amputations want to move a finger, they don’t really think about breaking a finger. They try to activate the muscles in the arm because that’s what the system reads. Because of that, these systems require a lot of training and practice. For our technology, because we directly interpret the nerve signal, it knows the patient’s intentions. If they want to move a finger, all they need to do is think about moving that finger. “

The technology team, led by the University of Minnesota, allows study participant Cameron Slavens to move the robotic arm using only his thoughts. Credit: Eva Daniels

Nguyen worked on this study for about 10 years with Associate Professor of Biomedical Engineering at the University of Minnesota Zhi Yang and was one of the key developers of neural chip technology.

The project began in 2012 when Edward Kiefer, a neurologist and CEO of Nerves, Incorporated, approached Young about creating a nerve implant that could benefit people with amputations. The couple has received funding from the U.S. Government’s Advanced Defense Research Projects Agency (DARPA) and has since conducted several successful clinical trials with real amputees.

Researchers have also partnered with the University of Minnesota’s Office of Technology Commercialization to create a startup called Fasikl – a play on the word “fascicle” that refers to a bundle of nerve fibers – to commercialize the technology.

“The fact that we can impact real people and one day improve the lives of patients is really important,” Nguyen said. “It’s fun to develop new technologies, but if you just do experiments in the lab, it doesn’t affect anyone directly. That’s why we want to be at the University of Minnesota and participate in clinical trials. For the past three or four years I’ve had the privilege of working with multiple human patients. I can be very emotional if I can help them move a finger or help them do things they didn’t think possible before. ”

Much of what makes the system so good compared to similar technologies is the inclusion of artificial intelligence, which uses machine learning to help interpret nerve signals.

Technology allows people with amputations to control the robot with the mind

Associate Professor of Biomedical Engineering at the University of Minnesota Zhi Yang shakes hands with study participant Cameron Slavens, who tested the robotic arm system of researchers. With the help of industry staff, researchers have developed a way to connect patient brain signals through a neural chip implanted in the arm, effectively reading a patient’s mind and opening the door to less invasive alternatives to brain surgery. Credit: Neuroelectronics Laboratory, University of Minnesota

“Artificial intelligence has tremendous potential to help explain many relationships,” Ian said. “This technology allows us to accurately record human data, neural data. With such neural data, the AI ​​system can fill in the gaps and determine what’s going on. It’s a really great thing to be able to combine this new chip technology with AI. It can help answer to many questions we could not answer before. “

The technology benefits not only people with amputations, but also other patients suffering from neurological disorders and chronic pain. Ian sees a future where invasive brain surgery will no longer be needed and brain signals can be obtained through peripheral nerve instead.

In addition, the implanted chip has an application that goes beyond medicine.

Right now to connect to the external AI and interface robotic arm. But if the chip could connect remotely to any computer, it would give people the ability to control their personal devices – such as a car or phone – with the mind.

“Some of these things are actually happening. A lot of research is moving from the so-called ‘fantasy’ category to the ‘scientists’ category,” Ian said. “It’s technology was designed for people with amputated limbs, but when it comes to its true potential, it may be applicable to all of us. ”

“How you have a hand again”: an ultra-precise prosthesis controlled by the mind

Additional information:
An Tuan Nguyen et al., A portable autonomous neuroprosthesis of the hand with finger control based on deep learning, Journal of Neural Engineering (2021). DOI: 10.1088 / 1741-2552 / ac2a8d

Citation: Technology allows people with amputation to control a robot with the mind (2022, May 18), obtained May 18, 2022 from .html

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