Doctors implanted a chip in Mr. Burkhart’s (24 year old with a 5 year old neck injury) brain two years ago. Seated in a lab with the implant connected through a computer to a sleeve on his arm, he was able to learn by repetition and arduous practice to focus his thoughts to make his hand pour from a bottle, and to pick up a straw and stir. He was even able to play a guitar video game.
“It’s crazy because I had lost sensation in my hands, and I had to watch my hand to know whether I was squeezing or extending the fingers,” Mr. Burkhart, a business student who lives in Dublin, Ohio, said in an interview. His injury had left him paralyzed from the chest down; he still has some movement in his shoulders and biceps.
The new technology is not a cure for paralysis. Mr. Burkhart could use his hand only when connected to computers in the lab, and the researchers said there was much work to do before the system could provide significant mobile independence.
But the field of neural engineering is advancing quickly. Using brain implants, scientists can decode brain signals and match them to specific movements. Previously, people have learned to guide a cursor on a screen with their thoughts, monkeys have learned to skillfully use a robotic arm through neural signals and scientists have taught monkeys who were partly paralyzed to use an arm with a bypass system. This new study demonstrates that the bypass approach can restore critical skills to limbs no longer directly connected to the brain.
Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic ‘neural bypass’ to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements1–11. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles12,13. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant’s forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5–C6) to the seventh cervical to first thoracic (C7–T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis
SOURCES – NY Times, Nature