Stanford University researchers have developed ReFIT, an algorithm that improves the speed and accuracy of neural prosthetics that control computer cursors. In a side-by-side comparison, the cursors controlled by the ReFIT algorithm doubled the performance of existing systems and approached the performance of a real arm. "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," says Stanford professor Krishna Shenoy.
The system uses a silicon chip that is implanted in the brain. The chip records "action potentials" in neural activity from several electrode sensors and sends the data to a computer. The researchers want to understand how the system works under closed-loop control conditions in which the computer analyzes and implements visual feedback taken in real time as the user neurally controls the cursor toward an onscreen target. The system can make adjustments in real time while guiding the cursor to a target, similar to how the hand and eye work in tandem to move a mouse cursor. The researchers designed the algorithm to learn from the user's corrective movements, allowing the cursor to move more precisely than in other systems.
From Stanford University
View Full Article
Abstracts Copyright © 2012 Information Inc., Bethesda, Maryland, USA
No entries found