This computer is learning to read your mind DIY Neuroscience a TED series
Translator: Joseph Geni
Reviewer: Krystian Aparta
Greg Gage: Mind-reading.
You’ve seen this in sci-fi movies:
machines that can read our thoughts.
However, there are devices today
that can read the electrical
activity from our brains.
We call this the EEG.
Is there information
contained in these brainwaves?
And if so, could we train a computer
to read our thoughts?
My buddy Nathan
has been working to hack the EEG
to build a mind-reading machine.
[DIY Neuroscience]
So this is how the EEG works.
Inside your head is a brain,
and that brain is made
out of billions of neurons.
Each of those neurons sends
an electrical message to each other.
These small messages can combine
to make an electrical wave
that we can detect on a monitor.
Now traditionally, the EEG
can tell us large-scale things,
for example if you’re asleep
or if you’re alert.
But can it tell us anything else?
Can it actually read our thoughts?
We’re going to test this,
and we’re not going to start
with some complex thoughts.
We’re going to do something very simple.
Can we interpret what someone is seeing
using only their brainwaves?
Nathan’s going to begin by placing
electrodes on Christy’s head.
Nathan: My life is tangled.
(Laughter)
GG: And then he’s going to show her
a bunch of pictures
from four different categories.
Nathan: Face, house, scenery
and weird pictures.
GG: As we show Christy
hundreds of these images,
we are also capturing the electrical waves
onto Nathan’s computer.
We want to see if we can detect
any visual information about the photos
contained in the brainwaves,
so when we’re done,
we’re going to see if the EEG
can tell us what kind of picture
Christy is looking at,
and if it does, each category
should trigger a different brain signal.
OK, so we collected all the raw EEG data,
and this is what we got.
It all looks pretty messy,
so let’s arrange them by picture.
Now, still a bit too noisy
to see any differences,
but if we average the EEG
across all image types
by aligning them
to when the image first appeared,
we can remove this noise,
and pretty soon, we can see
some dominant patterns
emerge for each category.
Now the signals all
still look pretty similar.
Let’s take a closer look.
About a hundred milliseconds
after the image comes on,
we see a positive bump in all four cases,
and we call this the P100,
and what we think that is
is what happens in your brain
when you recognize an object.
But damn, look at
that signal for the face.
It looks different than the others.
There’s a negative dip
about 170 milliseconds
after the image comes on.
What could be going on here?
Research shows that our brain
has a lot of neurons that are dedicated
to recognizing human faces,
so this N170 spike could be
all those neurons
firing at once in the same location,
and we can detect that in the EEG.
So there are two takeaways here.
One, our eyes can’t really detect
the differences in patterns
without averaging out the noise,
and two, even after removing the noise,
our eyes can only pick up
the signals associated with faces.
So this is where we turn
to machine learning.
Now, our eyes are not very good
at picking up patterns in noisy data,
but machine learning algorithms
are designed to do just that,
so could we take a lot of pictures
and a lot of data
and feed it in and train a computer
to be able to interpret
what Christy is looking at in real time?
We’re trying to code the information
that’s coming out of her EEG
in real time
and predict what it is
that her eyes are looking at.
And if it works, what we should see
is every time that she gets
a picture of scenery,
it should say scenery,
scenery, scenery, scenery.
A face – face, face, face, face,
but it’s not quite working that way,
is what we’re discovering.
(Laughter)
OK.
Director: So what’s going on here?
GG: We need a new career, I think.
(Laughter)
OK, so that was a massive failure.
But we’re still curious:
How far could we push this technology?
And we looked back at what we did.
We noticed that the data was coming
into our computer very quickly,
without any timing
of when the images came on,
and that’s the equivalent
of reading a very long sentence
without spaces between the words.
It would be hard to read,
but once we add the spaces,
individual words appear
and it becomes a lot more understandable.
But what if we cheat a little bit?
By using a sensor, we can tell
the computer when the image first appears.
That way, the brainwave stops being
a continuous stream of information,
and instead becomes
individual packets of meaning.
Also, we’re going
to cheat a little bit more,
by limiting the categories to two.
Let’s see if we can do
some real-time mind-reading.
In this new experiment,
we’re going to constrict it
a little bit more
so that we know the onset of the image
and we’re going to limit
the categories to “face” or “scenery.”
Nathan: Face. Correct.
Scenery. Correct.
GG: So right now,
every time the image comes on,
we’re taking a picture
of the onset of the image
and decoding the EEG.
It’s getting correct.
Nathan: Yes. Face. Correct.
GG: So there is information
in the EEG signal, which is cool.
We just had to align it
to the onset of the image.
Nathan: Scenery. Correct.
Face. Yeah.
GG: This means there is some
information there,
so if we know at what time
the picture came on,
we can tell what type of picture it was,
possibly, at least on average,
by looking at these evoked potentials.
Nathan: Exactly.
GG: If you had told me at the beginning
of this project this was possible,
I would have said no way.
I literally did not think
we could do this.
Did our mind-reading
experiment really work?
Yes, but we had to do a lot of cheating.
It turns out you can find
some interesting things in the EEG,
for example if you’re
looking at someone’s face,
but it does have a lot of limitations.
Perhaps advances in machine learning
will make huge strides,
and one day we will be able to decode
what’s going on in our thoughts.
But for now, the next time a company says
that they can harness your brainwaves
to be able to control devices,
it is your right, it is your duty
to be skeptical.