The algorithm's speed, accuracy and natural movement approach those of a real arm, researchers claim.
Stanford researchers have developed the algorithm for brain-implantable prosthetic systems, known as ReFIT, that vastly improves the speed and accuracy of neural prosthetics that control computer cursors.
In demonstrations with rhesus monkeys, cursors controlled by the new algorithm doubled the performance of existing systems and approached performance of the monkey's actual arm in controlling the cursor.
"These findings could lead to greatly improved prosthetic system performance and robustness in paralysed people, which we are actively pursuing as part of the FDA Phase-I BrainGate2 clinical trial here at Stanford," said researcher Krishna Shenoy, a professor at Stanford.
The system relies on a sensor implanted into the brain, which records "action potentials" in neural activity from an array of electrode sensors and sends data to a computer.
The frequency with which action potentials are generated provides the computer important information about the direction and speed of the user's intended movement.
The ReFIT algorithm that decodes these signals represents a departure from earlier models. In most neural prosthetics research, scientists have recorded brain activity while the subject moves or imagines moving an arm, analysing the data after the fact.
"Quite a bit of the work in neural prosthetics has focused on this sort of offline reconstruction," said Dr Vikash Gilja, the first author of the study, in a statement.
The system is able to make adjustments on the fly when guiding the cursor to a target, just as a hand and eye would work in tandem to move a mouse-cursor onto an icon on a computer desktop.
If the cursor were straying too far to the left, for instance, the user likely adjusts the imagined movements to redirect the cursor to the right.
The study was published in the journal Nature Neuroscience.
