First 'plug and play' brain prosthesis demoed in paralyzed person
Stable recordings let brain and machine learning system build
'partnership' over time

Date:
September 7, 2020
Source:
University of California - San Francisco
Summary:
In a significant advance, researchers working towards a
brain-controlled prosthetic limb at the UC San Francisco Weill
Institute for Neurosciences have shown that machine learning
techniques helped a paralyzed individual learn to control a computer
cursor using their brain activity without requiring extensive daily
retraining, which has been a requirement of all past brain-computer
interface (BCI) efforts.



FULL STORY ==========================================================================
In a significant advance, UC San Francisco Weill Institute for
Neurosciences researchers working towards a brain-controlled prosthetic
limb have shown that machine learning techniques helped an individual
with paralysis learn to control a computer cursor using their brain
activity without requiring extensive daily retraining, which has been
a requirement of all past brain- computer interface (BCI) efforts.


==========================================================================
"The BCI field has made great progress in recent years, but because
existing systems have had to be reset and recalibrated each day, they
haven't been able to tap into the brain's natural learning processes. It's
like asking someone to learn to ride a bike over and over again from
scratch," said study senior author Karunesh Ganguly, MD, PhD, an
associate professor in the UCSF Department of Neurology. "Adapting an artificial learning system to work smoothly with the brain's sophisticated long-term learning schemas is something that's never been shown before in
a person with paralysis." The achievement of "plug and play" performance demonstrates the value of so- called ECoG electrode arrays for BCI applicartions. An ECoG array comprises a pad of electrodes about the
size of a post-it note that is surgically placed on the surface of the
brain. They allow long-term, stable recordings of neural activity and have
been approved for seizure monitoring in epilepsy patients. In contrast,
past BCI efforts have used "pin-cushion" style arrays of sharp electrodes
that penetrate the brain tissue for more sensitive recordings but tend
to shift or lose signal over time. In this case, the authors obtained investigational device approval for long-term chronic implantation of
ECoG arrays in paralyzed subjects to test their safety and efficacy as long-term, stable BCI implants.

In their new paper, published September 7, 2020 in Nature Biotechnology, Ganguly's team documents the use of an ECoG electrode array in
an individual with paralysis of all four limbs (tetraplegia). The
participant is also enrolled in a clinical trial designed to test the
use of ECoG arrays to allow paralyzed patients to control a prosthetic
arm and hand, but in the new paper, the participant used the implant to
control a computer cursor on a screen.

The researchers developed a BCI algorithm that uses machine learning
to match brain activity recorded by the ECoG electrodes to the user's
desired cursor movements. Initially, the researchers followed the standard practice of resetting the algorithm each day. The participant would
begin by imagining specific neck and wrist movements while watching the
cursor move across the screen. Gradually the computer algorithm would
update itself to match the cursor's movements to the brain activity
this generated, effective passing control of the cursor over to the
user. However, starting this process over every day put a severe limit
on the level of control that could be achieved. It could take hours to
master control of the device, and some days the participant had to give
up altogether.

The researchers then switched to allow the algorithm to continue updating
to match the participant's brain activity without resetting it each
day. They found that the continued interplay between brain signals and the machine learning-enhanced algorithm resulted in continuous improvements
in performance over many days. Initially there was a little lost ground
to make up each day, but soon the participant was able to immediately
achieve top level performance.

"We found that we could further improve learning by making sure that the algorithm wasn't updating faster than the brain could follow -- a rate
of about once every 10 seconds," said Ganguly, a practicing neurologist
with UCSF Health and the San Francisco Veterans Administration Medical
Center's Neurology & Rehabilitation Service. "We see this as trying to
build a partnership between two learning systems -- brain and computer
-- that ultimately lets the artificial interface become an extension of
the user, like their own hand or arm." Over time, the participant's
brain was able to amplify patterns of neural activity it could use to
most effectively drive the artificial interface via the ECoG array,
while eliminating less effective signals -- a pruning process much like
how the brain is thought to learn any complex task, the researcher
say. They observed that the participant's brain activity seemed to
develop an ingrained and consistent mental "model" for controlling the
BCI interface, something that had never occurred with daily resetting
and recalibration. When the interface was reset after several weeks of continuous learning, the participant rapidly re-established the same
patterns of neural activity for controlling the device -- effectively retraining the algorithm to its former state.

"Once the user has established an enduring memory of the solution for controlling the interface, there's no need for resetting," Ganguly
said. "The brain just rapidly convergences back to the same solution." Eventually, once expertise was established, the researchers showed they
could turn off the algorithm's need to update itself altogether, and the participant could simply begin using the interface each day without any
need for retraining or recalibration. Performance did not decline over
44 days in the absence of retraining, and the participant could even
go days without practicing and see little decline in performance. The establishment of stable expertise in one form of BCI control (moving
the cursor) also allowed researchers to begin "stacking" additional
learned skills -- such as "clicking" a virtual button - - without loss
of performance.

Such immediate "plug and play" BCI performance has long been a goal
in the field, but has been out of reach because the "pincushion-style" electrodes used by most researchers tend to move over time, changing the signals seen by each electrode. Also, because these electrodes penetrate
brain tissue, the immune system tends to reject them, gradually impairing
their signal. ECoG arrays are less sensitive than these traditional
implants, but their long-term stability appears to compensate for this shortcoming. The stability of ECoG recordings may be even more important
for long-term control of more complex robotic systems such as artificial
limbs, a key goal of the next phase of Ganguly's research.

"We've always been mindful of the need to design technology that doesn't
end up in a drawer, so to speak, but which will actually improve the
day-to-day lives of paralyzed patients," Ganguly said. "These data show
that ECoG-based BCIs could be the foundation for such a technology."

========================================================================== Story Source: Materials provided by
University_of_California_-_San_Francisco. Original written by Nicholas
Weiler. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Daniel B. Silversmith, Reza Abiri, Nicholas F. Hardy, Nikhilesh
Natraj,
Adelyn Tu-Chan, Edward F. Chang, Karunesh Ganguly. Plug-and-play
control of a brain-computer interface through neural
map stabilization. Nature Biotechnology, 2020; DOI:
10.1038/s41587-020-0662-5 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/09/200907112335.htm

--- up 2 weeks, 6 hours, 50 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1337:3/111)