US team touts robotic arm mind-controlled sans brain implants

Photo courtesy of Carnegie Mellon University

Using a noninvasive brain-computer interface (BCI), researchers from Carnegie Mellon University and University of Minnesota have developed the first-ever successful mind-controlled robotic arm that can continuously track and follow a computer cursor, without relying on invasive brain implants.

Being able to noninvasively control robotic devices using only thoughts will have broad applications, in particular benefiting the lives of paralyzed patients and those with movement disorders.

Until now, BCIs successful in controlling robotic arms have used invasive brain implants. These implants require a substantial amount of medical and surgical expertise to correctly install and operate, not to mention cost and potential risks to subjects, and as such, their use has been limited to just a few clinical cases.

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However, BCIs that use noninvasive external sensing, rather than brain implants, receive “dirtier” signals, leading to current lower resolution and less precise control. Thus, when using only the brain to control a robotic arm, a noninvasive BCI doesn’t stand up to using implanted devices.

“(N)oninvasive is the ultimate goal,” said Bin He, Trustee Professor and Department Head of Biomedical Engineering at Carnegie Mellon University. “Advances in neural decoding and the practical utility of noninvasive robotic arm control will have major implications on the eventual development of noninvasive neurorobotics.”

Using a noninvasive BCI to control a robotic arm that’s tracking a cursor on a computer screen, for the first time ever, He has shown in human subjects that a robotic arm can now follow the cursor continuously.

Robotic arms controlled by humans noninvasively had previously followed a moving cursor in jerky, discrete motions—as though the robotic arm was trying to “catch up” to the brain’s commands— but now, the arm follows the cursor in a smooth, continuous path.

The technology has, to date, been tested in 68 able-bodied human subjects (up to 10 sessions for each subject), including virtual device control and controlling of a robotic arm for continuous pursuit. The technology is directly applicable to patients, and the team plans to conduct clinical trials in the near future.

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