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OK, so
maybe you are always
wondered how you could do Jedi mind
tricks with a computer, and
that's exactly why we're here now.
So Agnew is going to
tell you the fundamentals
of EEG based brain computer interface,
and he's always been
fascinated with the human brain and
he is a researcher and that scope
and I give the stage to you.
Hello.
And the reason why I'm giving this talk
is recently there
has been a development and
electroencephalography that was
developed about 100 years ago
and has been used in research
and in medicine as well.
But we now have
consumer grade EEG headsets,
as well as some open hardware
projects aiming
to develop EEG headsets.
There I have a picture of the
amount of EPOC, which I think was the
first consumer great EEG
headset.
And actually,
I think the aim of the Open
BCI project is to
get cheap
research grade hardware.
I'm not going to explain too much
about about
the devices.
I want to talk a bit about
how we can use EEG
readings to have to have
a brain computer interface.
A brain computer interface
typically consists of a user having
a task, the task can be
thinking, for example, to it
to have some input to
if if it's used to
to drive an electric wheelchair, for
example, it could be Atholl to go
forward.
The signal has to be the EEG signal
has to be acquired.
I'm not going to talk about
that. I'm more focusing on the
pre processing of the data and the
feature. Extraction classification
can generally be done with all kinds of
classifiers popular
support vector machines.
Um, but a good feature.
Extraction is essential.
We cannot really do
machine learning approaches where we
learn the features because we typically
do not have very much training data
doing e.g. experiments with human
subjects. Takes a lot of time
and also
the data might contain
private information.
So often, the datasets are not
made public.
So yeah, that's what I'm going to talk
about. Mainly, but generally after
classification, we have an open
translation that can be a virtual
keyboard or something, and
a can be that is optional, a feedback
that allows the user to a trained brain
computer interface.
Um, here
I'm showing you the timeline
of an EEG signal.
This was a resting state experiment.
So the subject was just resting.
Doing nothing was eyes open.
Um,
you can't see very much on the table and
it looks quite random.
Random oscillations quite low.
Generally, the signals are in some
milli void range.
Um, so one of the first
steps that you can do to a time
frequency analysis.
Um, so what we have here is we have 14
seconds of EEG
and 14 electrodes
that the 14 channels as the
emotive epoch AEG's headset
has.
Um. And here we also have 14 seconds.
And this is basically
doing a
couple of get
computing
spectrum for different time
slots. So you see the development
of the spectrum over time.
And what you see here is one of the
things that make it difficult.
Most of the signal power
is in the range below
five hertz.
The different frequency ranges are
typically associated with,
um, for example,
some states of mind like sleep
states.
Actually, it's quite easy even with the
timeline to to
see and which sleep state someone is
important. This is the
alphabet, and
that's something that should be in.
And the plot that I showed before as
well, um,
we couldn't really see that in the
timeline if actually I had instructed
the subject to have the eyes closed
but not open.
Um, we would have seen oscillations
in that range on the
electrodes that are
above the visual cortex
because they would
get all to idle state as if
nothing is seen.
And we would have more power
in the
UM if we are doing e.g.
experiments.
Um, we
typically look into changes because we
have this huge random noise signal
where we have basically
no idea what it means.
We typically have experiments where we
look at some, um,
at at least two different states.
So we define something as
a baseline and then we typically
look at what changes if we, for
example, we have a resting state task
for the subject.
And then the task is to think
maybe the um,
the command to move the VHF.
Um, what we see
here is again, from the same
EEG recording.
Um,
what I did was I used the
two seconds before that I plotted here,
but it looks about the same
uh, computed the average and
um, divided it all by
it.
We call that baseline
correction.
So now
it's easier to see changes here and
in the other areas.
So having
a baseline is something that you
normally do if you have some EEG
experiments.
We also have some other problems besides
General Becnel background noise
that we have.
It's artifacts here.
It's a similar
timeline.
The difference is the
subject was instructed to blink
and intervals.
You see that there are some huge peaks
there, especially on
the lower and the higher lines.
That's because, according to
the 10 20 system,
the electrodes are numbered around the
head. So on the top and on the
bottom, it's basically the electrodes
on the forehead.
So what we see there is I artifacts
actually to see
artifacts.
That's quite easy in the timeline.
The problem is getting rid of it
if we don't want to have it.
So we start typically by instructing
subjects not to blink,
not to move.
Also, a problem
can be having.
For example, the 50 hard power
grid
can also be an artifact in the scanner.
Therefore, we typically have
filters at
its frequency.
There are different approaches to
get rid of the artifacts.
The simplest one is cutting out of the
data.
The parts with artifacts.
But this basically means we have to
repeat the experiment to have it several
times.
But we are doing that anyway.
Um, if we.
One approach for brain computer interface
are event related potentials.
If we have an event that could be a
subject is shown a picture
or any other stimuli,
and we repeat that.
And then we everard's
of all the reputations of showing this
image.
Then all the random noise will
cancel out.
And what we are left with is
the EEG component that actually
depends on the processing
of showing this image
and that we call an event relentlessly
related potential.
This is just an example.
Typically, it doesn't look that nice.
We typically columns the peaks,
so we have three
positive peaks here we call P1,
P2, P3 and the P3
also called P3 100 because it's
about 300 milliseconds after the
stimulus.
That is something that we use for brain
computer interfaces.
Um,
because there
is the so-called oddball paradigm.
The P3 100 is only there
if something is relevant to your task
and it's not happening very
often.
So the P300 speller, which
is something like a virtual
keyboard. Unfortunately, I don't have an
animation.
The lines and rows would light
up at at
some speed.
If you want to type a letter,
you would focus on a letter and when
it lights up,
that is a real and relevant
event to you
because you want to type it.
So exactly in that case, you
will have
the P300 in your ERP.
And of course,
the system that is providing the stimuli
as recordings, the timing and then north
which which letter actually
was lit up when this
appeared.
So this way you can Typekit things.
But again, you would need tool to
stare at one letter for a while
because it has to be repeated
several times.
So I want to present
more specialized piece 100 based
brain computer interface that
is an authentication scheme.
The idea is we are having 100
normal photos can be anything
and we select some which
are all passport.
So this is the example that actually
has had that use for the experiment.
Five very different photos.
So now I'm doing a small experiment was
you try to
remember those photos and
try to see them
and count them in
this video stream.
So I think I'm not
asking you to raise your arms because I
come to you anyway.
I guess some of you
might have seen all five pictures.
My son might have counted less
for the first time. The task is not
really easy. But generally
when in this experiment, you you
counted something, you would probably
have had this piece thrown in your brain.
Those are the results of the experiment.
I hope you can read it
might be a bit too dark
as we have 95 non-target
image and only five percent.
So five target images.
We use the F1
score to evaluate
the classifier.
We did cross validation on the data here
where we have the best score.
We also did
train the classifier, which was a
simple, linear discriminant
analysis.
We trained just by
the experiment done by one person,
and then we also tried to
do a general classifier that
we trained with other people's
data. For that, we used the best datasets
that we have that had the highest
score and the cross validation.
It still works.
The interesting thing here is that
the classification of the IG data
as possible without tuning the classifier
to the user
making the system non biometric.
This is the number of trials
that we average, so we actually scored
50 of those bursts that you have seen
just before.
That takes about 20 minutes,
and no one wants to use
an authentication system where one
lock in takes 20 minutes.
So we looked at how many
birds do we actually need.
But it seems like up to 50.
It still increases, and there
is a huge difference between different
sets or the highest line,
as is the top rated
subjects and zeros are
much lower. So it depends
a lot on the subject.
Maybe also on how was
EEG headset fit and of
maybe how was I focused on the task?
Um
yeah. But it's the
biggest effect that we found was,
um, for an authentication
system, we want to have permanence.
So if we want to lock in again
after a few months, it should still work.
So we had three sessions
with some months in between.
And actually, we got better schools over
time.
We feared that they might degrade, but it
seems there a training effect.
Um yeah. And the signal is permanent
enough.
So here is the final score.
That's a plot.
Because even though it's no real
biometric system, um,
we are measuring biosignatures
and that can go wrong.
Therefore, we have false acceptance rates
and false rejection rates,
and we are coming to an equal error rate
of about 10 percent, which is, of
course, too bad, especially when you'll
see that one authentication
round takes about 20 minutes.
Um
yeah. So the idea was if
we have the we are showing
the images very fast, we can
have a very sort
of lock in time.
But it actually didn't really work well
enough.
And
that's it.
There are three minutes left.
Are there questions?
Oh, I found my voice again, so
indeed, we have a bit of time.
There are two microphones and the
internets, so remember and
that's a question at that microphone.
Hello, there.
I was wondering, have you heard of
this being used to detect terrorists?
There was a there is an experiment done
where they showed images of things
that you're not supposed to know as as a
regular citizen.
So they would show all these innocent
images. And then there would also be
a blasting cap or the magazine
of an AK 47 or stuff
that you're supposed to know if you went
to a terrorist training camp and
they would do the exact same P300
thing. I thought that was interesting.
And of course, if you read about,
you know, about training camps and
terrorists, you would fail the tests,
which would be interesting.
Yeah, um, I
haven't heard about the application on
terrorism, but very similar to the
application on criminal
investigations as a lie detector based
on Piece 100.
So basically the same,
you are showing some pictures that only
the only
the guilty
knowledge tests.
Yeah. Yeah, that that only the one who
is guilty knows.
And but there are also
some papers about if you are
if you are attacked by this piece for 100
based guilty knowledge test, how to
how to prevent being detected.
OK, now the question that microphone
I
have two small questions.
One is which was the EEG headset
used in the image based authentication.
Was it the open BCI?
And then also is P300
like individualize like, does it need a
lot of calibration or is it something
that you can just detect leaks straight
in?
So the eggheads had used
for for the experiments was the emotive
epoch,
and the
P300 seems to be a bit individual.
There are approaches doing biometrics
by looking at P300.
But our approach was to have a general
P300 detector that would work
on anyone. That was the difference
between the ICC individual classifier
and a
general classifier.
You can see the score difference here,
the middle and the right one, and the
left four was a cross validation.
OK, is there a question from the
internet? I'm looking at the no question
from the internet.
Um, so unfortunately, the time is
running out, and so I have to ask
you to approach security directly.
And I don't know.
You're here, you're at the Congress.
Yes, can be contacted in some way,
I guess.
OK, then I would say thanks
again for his time.