New Technique Allows Scientists to Read Minds at Nearly the Speed of Thought
New Technique Allows Scientists to Read Minds at Nearly
the Speed of Thought
By George Dvorsky January 29, 2016 12:16pm
New Technique Allows Scientists to Read Minds at Nearly
the Speed of Thought
An experiment by University of Washington researchers is
setting the stage for advances in mind reading technology. Using brain implants
and sophisticated software, researchers can now predict what their subjects are
seeing with startling speed and accuracy.
The ability to view a two-dimensional image on a page or
computer screen, and then transform that image into something our minds can
immediately recognize, is a neurological process that remains mysterious to
scientists. To learn more about how our brains perform this task—and to see if
computers can collect and predict what a person is seeing in real time—a
research team led by University of Washington neuroscientist Rajesh Rao and
neurosurgeon Jeff Ojermann demonstrated that it’s possible to decode human brain
signals at nearly the speed of perception. The details of their work can be
found in a new paper in PLOS Computational Biology.
The team sought the assistance of seven patients
undergoing treatment for epilepsy. Medications weren’t helping alleviate their
seizures, so these patients were given temporary brain implants, and electrodes
were used to pinpoint the focal points of their seizures. The UW researchers
saw this as an opportunity to perform their experiment. “They were going to get
the electrodes no matter what,” noted Ojermann in a UW NewsBeat article. “We
were just giving them additional tasks to do during their hospital stay while
they are otherwise just waiting around.”
The patients were shown a random sequence of
pictures—images of human faces, houses, and blank gray screens—on computer
monitors in brief 400 millisecond intervals. Their specific task was to watch
for an image of an upside-down house.
New Technique Allows Scientists to Read Minds at Nearly
the Speed of Thought
At the same time, the electrodes in their brain were
connected to software that extracted two distinct brain signal properties,
namely “event related potentials” (when massive batches of neurons
simultaneously light up in response to an image) and “broadband spectral”
changes (signals that linger after viewing an image).
As the images flickered on the screen, a computer sampled
and digitized the incoming brain signals at a rate of 1,000 times per second.
This resolution allowed the software to determine which combination of
electrode locations and signals correlated best to what the patients were
seeing. “We got different responses from different (electrode) locations; some
were sensitive to faces and some were sensitive to houses,” Rao said.
After training the software, researchers exposed the
patients to an entirely new set of pictures. Without previous exposure to these
new images, the computer was able to predict with 96 percent accuracy when a
test subject was seeing a house, a face, or a grey screen. And it did so at
nearly the speed of perception.
This proficiency only occurred when the computer
considered both event-related potentials and broadband changes, which as stated
in the study, suggests “they capture different and complementary aspects of the
subject’s perceptual state.” So when it comes to understanding how a person
perceives a complex visual object, it’s important to consider the “global
picture” of large neural networks.
While interesting, the results of the study are
exceptionally limited. A true test of the system would be to see if it could
learn a much larger set of images, including different categories. It’s not
immediately obvious, for example, if the computer could discern if a patient
was viewing the face of a human or a dog.
Once refined, however, this kind of brain decoding could
be used to build communication mechanisms for “locked-in” patients who are
paralyzed or have suffered a stroke. This technique could also assist with
brain mapping, allowing neuroscientists to identify locations in the brain
responsible for certain types of information in real time.
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