Researchers developed an algorithm that
vastly improves the speed and accuracy of
neural prosthetics that control computer
cursors; © Vikash Gilja/Stanford University
When a paralyzed person imagines moving a limb, cells in the part of the brain that controls movement still activate as if trying to make the immobile limb work again.
Despite neurological injury or disease that has severed the pathway between brain and muscle, the region where the signals originate remains intact and functional. In recent years, neuroscientists and neuroengineers working in prosthetics have begun to develop brain-implantable sensors that can measure signals from individual neurons, and after passing those signals through a mathematical decode algorithm, can use them to control computer cursors with thoughts. The work is part of a field known as neural prosthetics.
A team of Stanford researchers have now developed an algorithm, known as ReFIT, that vastly improves the speed and accuracy of neural prosthetics that control computer cursors. In side-by-side 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 Phase-I BrainGate2 clinical trial here at Stanford," said Krishna Shenoy.
The system relies on a silicon chip 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 key 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, analyzing the data after the fact. "Quite a bit of the work in neural prosthetics has focused on this sort of offline reconstruction," said Vikash Gilja.
The Stanford team wanted to understand how the system worked "online," under closed-loop control conditions in which the computer analyzes and implements visual feedback gathered in real time as the monkey neurally controls the cursor to toward an onscreen target.
The system is able to make adjustments on the fly when while 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 their imagined movements to redirect the cursor to the right. The team designed the system to learn from the user's corrective movements, allowing the cursor to move more precisely than it could in earlier prosthetics.
To test the new system, the team gave monkeys the task of mentally directing a cursor to a target — an onscreen dot — and holding the cursor there for half a second. ReFIT performed vastly better than previous technology in terms of both speed and accuracy. The path of the cursor from the starting point to the target was straighter and it reached the target twice as quickly as earlier systems, achieving 75 to 85 percent of the speed of real arms. "This paper reports very exciting innovations in closed-loop decoding for brain-machine interfaces. These innovations should lead to a significant boost in the control of neuroprosthetic devices and increase the clinical viability of this technology," said Jose Carmena.
REHACARE.de; Source: Stanford School of Engineering
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