When a paralyzed person imagines moving a limb, the brain part that controls movement acts as if it is activating the immobile limb.
Despite neurological injury or disease that has severed the pathway between brain and muscle, the region where the signals originate remains intact and functional.
Neuroscientists and neuroengineers from Stanford have started working on the project, which is part of a field known as neural prosthetics, the journal Nature Neuroscience reports.
The team has developed an algorithm, known as ReFIT, that vastly improves the speed and accuracy of neural prosthetics that control computer cursors, according to a Stanford statement.
Krishna Shenoy, professor of electrical engineering, bioengineering and neurobiology at Stanford, collaborated on the project with a team led by research associate Vikash Gilja and bioengineering doctoral candidate Paul Nuyujukian.
In demonstrations with rhesus monkeys, cursors controlled by the ReFIT algorithm doubled the performance of existing systems and approached performance of the real arm.
Better yet, more than four years after implantation, the new system is still going strong, while previous systems have seen a steady decline in performance over time.
"These findings could lead to greatly improved prosthetic system performance and robustness in paralyzed people, which we are actively pursuing as part of the FDA (Food and Drug Administration) Phase-I BrainGate2 clinical trial here at Stanford," said Shenoy.