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.
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 paralysed people, which we are actively pursuing as
part of the FDA Phase-I BrainGate2 clinical trial here at Stanford,"
said Krishna Shenoy, a professor of electrical engineering,
bioengineering and neurobiology at Stanford.
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.
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
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, associate professor of
electrical engineering and neuroscience at 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 team introduced a second innovation in the way ReFIT encodes information about the
position
and velocity of the cursor. Gilja said that previous algorithms could
interpret neural signals about either the cursor's position or its
velocity, but not both at once. ReFIT can do both, resulting in faster,
cleaner movements of the curso
The results have been published in the journal
Nature Neuroscience.
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