- New algorithm has achieved greatest ever speed and accuracy in translating neurological signals into movement
- The system relies on a silicon chip implanted on the brain and could one day be used to control prosthetic limbs
- But scientists have so-far focused on the less demanding but still useful task of controlling a computer cursor
By Damien Gayle
PUBLISHED: 12:30 EST, 19 November 2012 | UPDATED: 12:30 EST, 19 November 2012
Gentlemen, we can rebuild him. We have the technology – well, almost.
The science fiction behind the Six Million Dollar Man took a step closer to science reality yesterday when researchers announced a breakthrough in thought control of computers.
Researchers from Stanford University in California have developed an algorithm that translates the neurological signals for movement with greater than ever speed and accuracy.
The system, which relies on a silicon chip implanted in the brain, has been used to allow monkeys to control computer cursors – but could one day control prosthetic limbs.
‘These findings could lead to greatly improved prosthetic system performance and robustness in paralysed people,’ said Krishna Shenoy, who led the team that carried out the research.
When a paralysed 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, the researchers explain.
Even where a neurological injury or disease has severed the pathway between brain and muscle, the region where the signals originate often remains intact and functional.
In the Six Million Dollar Man injured astronaut Steve Austin is rebuilt by U.S. scientists who use a range of bionic implants which improve his strength, speed and vision to superhuman standards.
He can run at speeds of 60 mph, his eye has a 20:1 zoom lens and infrared capabilities, while his limbs all have the equivalent power of a bulldozer.
He uses his enhanced abilities to work for the OSI (Office of Scientific Intelligence) as a secret agent. Until now, such technology has been merely a pipe dream.
Previous research into the field of neural prosthetics has begun to develop brain-implantable sensors able to measure signals from individual neurons, interpret them, and use them to control computer cursors using thoughts alone.
But the new algorithm developed at Stanford, known as ReFIT, vastly improves the speed and accuracy of the control, the researchers reported on the November 18 issue of the Journal Nature Neuroscience.
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, the researchers claim.
Better still, 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 Stanford 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, analysing 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 first author of the paper.
IT WORKS – ON MONKEYS, AT LEAST
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 per cent 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, of the University of California Berkeley.
Critical to ReFIT’s time-to-target improvement was its superior ability to stop the cursor. While the old model’s cursor reached the target almost as fast as ReFIT, it often overshot the destination, requiring additional time and multiple passes to hold the target.
The key to this efficiency was in the step-by-step calculation that transforms electrical signals from the brain into movements of the cursor onscreen. The team had a unique way of ‘training’ the algorithm about movement. When the monkey used his real arm to move the cursor, the computer used signals from the implant to match the arm movements with neural activity.
Next, the monkey simply thought about moving the cursor, and the computer translated that neural activity into onscreen movement of the cursor. The team then used the monkey’s brain activity to refine their algorithm, increasing its accuracy.
The Stanford team instead wanted to understand how the system worked ‘online’, under closed-loop control conditions in which the computer analyses 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.
A second innovation was the way that ReFIT encodes information about the speed and velocity of the cursor. Dr Gilja explained that previous algorithms could interpret neural signals about either speed or velocity, but not both at once. ReFIT can do both, resulting in faster, cleaner movements of the cursor
James Gnadt, program director in Systems and Cognitive Neuroscience at the U.S. National Institute of Neurological Disorders and Stroke, hailed the breakthrough.
‘Despite great progress in brain-computer interfaces to control the movement of devices such as prosthetic limbs, we’ve been left so far with halting, jerky, Etch-a-Sketch-like movements,’ he said.
‘Dr Shenoy’s study is a big step toward clinically useful brain-machine technology that have faster, smoother, more natural movements.’
For the time being, the team has focused on improving cursor movement rather than the creation of robotic limbs, but that is not out of the question, Dr Gilja said.
Near term, precise, accurate control of a cursor is a simplified task with enormous value for paralyzed people. ‘We think we have a good chance of giving them something very useful,’ he said.