Monitoring electroencephalograms with
the help of artificial intelligence makes it possible to determine the preferences
of large groups of people from just their brain activity.
From:
University of Helsinki
June
17, 2020 -- Researchers at the University of Helsinki have developed a
technique, using artificial intelligence, to analyse opinions, and draw
conclusions using the brain activity of groups of people. This technique, which
the researchers call "brainsourcing," can be used to classify images
or recommend content, something that has not been demonstrated before.
Crowdsourcing
is a method to break up a more complex task into smaller tasks that can be distributed
to large groups of people and solved individually. For example, people can be
asked if an object can be seen in an image, and their responses are used as
instructional data for an image recognition system. Even the most advanced
image recognition systems based on artificial intelligence are not yet fully
automated. Instead, training them requires the opinions of several people on
the content of many sample images.
The
University of Helsinki researchers experimented with the possibility of
implementing crowdsourcing by analysing people's electroencephalograms (EEGs)
with the help of AI techniques. Rather than asking for people's opinions, this
information could be read directly from the EEG.
"We
wanted to investigate whether crowdsourcing can be applied to image recognition
by utilising the natural reactions of people without them having to carry out
any manual tasks with a keyboard or mouse," says Academy Research Fellow
Tuukka Ruotsalo from the University of Helsinki.
Computers
classify images
In
the study, a total of 30 volunteers were shown images of human faces on a
computer display. The participants were instructed to label the faces in their
mind based on what was portrayed in the images. For example, whether an image
portrayed a blond or dark-haired individual, or a person smiling or not
smiling. Unlike in conventional crowdsourcing tasks, they did not provide any
additional information using the mouse or keyboard -- they simply observed the
images presented to them.
Meanwhile,
the brain activity of each participant was collected using
electroencephalography. From the EEGs, the AI algorithm learned to recognise
images relevant to the task, such as when an image of a blond person appeared
on-screen.
In
the results of the experiment, the computer was able to interpret these mental
labels directly from the EEG. The researchers concluded that brainsourcing can
be applied to simple and well-defined recognition tasks. Highly reliable
labelling results were already achieved using data collected from 12 volunteers.
User-friendly
techniques are on the way
The
findings can be utilised in various interfaces that combine brain and computer
activity. These interfaces would require the availability of lightweight and
user-friendly EEG equipment in the form of wearable electronics, as opposed to
the equipment used in the study, which requires a trained technician.
Lightweight wearables that measure EEG are actively being developed and may be
available sometime in the near future.
"Our
approach is limited by the technology available," says Keith Davis, a
student and research assistant at the University of Helsinki.
"Current
methods to measure brain activity are adequate for controlled setups in a
laboratory, but the technology needs to improve for everyday use. Additionally,
these methods only capture a very small percentage of total brain activity. As
brain imaging technologies improve, it may become possible to capture
preference information directly from the brain. Instead of using conventional
ratings or like buttons, you could simply listen to a song or watch a show, and
your brain activity alone would be enough to determine your response to
it."
https://www.sciencedaily.com/releases/2020/06/200617150003.htm