Washington, November 19 (ANI): 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.
Now, a team of Stanford researchers including Indian origins have developed an algorithm, known as ReFIT, that vastly improves the speed and accuracy of neural prosthetics that control computer cursors.
Research associate Dr. Vikash Gilja and bioengineering doctoral candidate Paul Nuyujukian led the team.
Perfecting the computer algorithm for this translation has proven a challenge. Recently, however, researchers at Stanford University have designed an algorithm, known as ReFIT, that vastly improves the speed and accuracy of neural prostheses that control computer cursors.
The advantage of the ReFIT technology is that it allows the system to make immediate adjustments while guiding the cursor to a target, just as a hand and eye work together to move a mouse-cursor onto an icon on a computer desktop.
According to the press release:
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….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 efficiency of cursor movement ranged from between 75-85 percent of the speed of real arms – a remarkable advancement.
Check out a video demonstrating how this algorithm offers an improvement on previous technologies:
