After years of paralysis, Ian Burkhart controls his hand with new technology that reads signals from his brain and delivers them to a sleevelike device on his arm.

After years of paralysis, Ian Burkhart controls his hand with new technology that reads signals from his brain and delivers them to a sleevelike device on his arm.

Ohio State University Wexner Medical Center/Batelle

Brain implant helps quadriplegic play Guitar Hero

After Ian Burkhart broke his neck and lost all movement below his shoulders at age 19, his brain still told his hands how to move—but the messages couldn’t get through his severed spinal cord. Now, thanks to recent advances in electrical stimulation technology, Burkhart can once again grasp, pour, swipe a credit card, and even play Guitar Hero. To do so, he uses a microelectrode array that reads his brain’s signals and sends them through wires to a gel sleeve that electronically stimulates his muscles.

“This is the first time [stimulated movement has] been linked to signals recorded from within the brain,” says biomedical engineer Chad Bouton, one of the study’s authors and vice president of advanced engineering and technology at the Feinstein Institute for Medical Research in Manhasset, New York. “Now, the patient is able to control movements in his hand with his own thoughts.”

In the past, researchers have used several approaches to give paralyzed patients control of their hands. In some systems, researchers implanted sensors in shoulder muscles that patients could still control, allowing them to move one hand by contracting muscles in the opposite shoulder. Other systems have controlled hand movement using electroencephalography (EEG) brain recordings taken outside the scalp. Still other technologies use brain implants similar to those in the new study, but to control robotic arms, external skeletons, or computer cursors, rather than the patient’s own muscles. Never before has a paralyzed patient been able to precisely move his hand using the same neural signals that controlled his hand before his injury. 

After implanting the microarray in Burkhart’s brain, researchers connected it to a computer equipped with a machine-learning algorithm, which they connected in turn to a polymer gel sleeve on Burkhart’s forearm. The sleeve has 130 electrodes that deliver signals to his muscles without penetrating the skin. Burkhart trained the system to connect patterns of neural signals to specific movements by repeatedly mirroring the movements of an imaginary hand on a computer screen.

Soon, Burkhart could manipulate large objects like glasses and use finer motor skills to lift and move small objects like straws. He could also move each finger independently, a feat that other systems had never achieved. When the researchers tested Burkhart using a widely accepted system for classifying the abilities of quadriplegics, they found he could use his hand just as well as someone with a much lesser injury, according to the study published today in Nature. Now, 2 years after their first training session, Burkhart and the machine learning software continue to improve together, Rezai says.

But the system isn’t ready for home use yet. To use his hand, Burkhart must travel to the lab, where he is hooked up to a tabletop full of bulky equipment, such as the computer that runs the algorithm. The researchers hope that this proof-of-concept study could lead to smaller devices that wirelessly transmit brain signals to wearable, muscle-stimulating garments. “You can imagine gloves, socks, pants—anything that can be incorporated into different garments that conform to your muscles externally,” Rezai says, though he says such devices are a long way off.

Some researchers favor systems that, unlike the new one, implant electrodes directly into the muscles, giving patients more precise control. “The performance of implanted systems is an order of magnitude better than surface stimulation,” says Robert Kirsch, a biomedical engineer at Case Western Reserve University in Cleveland, Ohio, who leads the team that developed the system using shoulder muscles. “We moved away from surface stimulation about 40 years ago.”

Another drawback of external muscle stimulators is that the high voltage needed to get through the skin can mess up the signals being recorded in the brain, says Lee Miller, a neuroscientist and biomedical engineer at Northwestern University in Evanston, Illinois, who was not involved in the project. Miller says he believes the researchers will have to implant the electrodes in peoples’ forearms to bring their technology to the next level.

Kirsch also doubts whether patients who can still move their shoulders should have microarrays implanted in their brains. Systems with implanted microarrays show promise—indeed, Kirsch’s team is developing one of its own—but in his view, they are more suitable for patients who are more severely handicapped. “For the level of person that they studied, there are many alternatives” that don’t require brain surgery, Kirsch says.

Although alternative systems may not involve brain surgery, they often require multiple other surgeries, such as connecting devices to muscles in the shoulder and embedding electrodes in the forearm, Rezai says. The only surgery needed for Rezai and Bouton’s neural bypass system is implantation of the microarray in the brain.

“The movement forward should be less invasive, less surgeries, and more simple solutions,” Rezai says. “As technology evolves, we'll get there.”