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, which 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. 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. 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.
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