hello everybody
my name is Maurice Rekrut i'm a
researcher at the German Research Center
for Artificial Intelligence
and i want to present to you today our
recent work about Spinning Icons
introducing a novel SSVEP BCI paradigm
based on rotation. So what is SSVEP, SSVEP
stands for Steady State Visually Evoked
Potential
and it is actually a brain response
which is evoked when you are looking at
visual stimuli which are flickering in a
certain frequency
You can measure this potential at the
back of your head at your
primary visual cortex the occipital
region
and if you imagine this checkerboard
pattern now be
oscillating in a certain frequency let
it be three hertz you're looking at it
if you measure the Electroencephalogram
at the back of the head
transfer it into the frequency domain
you can see a peak at stimulus frequency
and at its harmonics so if it is a
stimulus frequency of 3 it would be 6
9 12 and so on
By doing so you can actually
realize brain computer interfaces
because you measure brain activity
and can detect at which stimulus a
person was looking at
the advantages of this technology are
that it's quite well researched robust
it has a high classification accuracy
and it requires
only very little and actually sometimes
no training
so where is the problem with it if you
see this video about the state of the
art currently
there is a person sitting you can see it
on the left wearing an EEG
and looking at a screen with a keyboard
where all the characters are flickering
in different frequencies
and he manages to type quite fast here
you can see high speed bci
with this technology but i think the
problem is obvious that if
you imagine you had to type your paper
for this conference with such a
technology you would probably go crazy
because it's flickering all the time
it's annoying it's causing a lot of
visual stress and fatigue
and currently researchers try to tackle
this problem by
disguising this flickering into
motion so we have here a paper of
Yan et. al already tried to create four
novel motion based paradigms for Steady
State visually evoked potentials
you can see a flipping stick here on the
left rotating
circles which actually also managed to
induce SSVEPs
but those concepts are quite abstract so
imagine to use it in a UI you're
quite limited to how you can use it
and we therefore propose the method of
spinning icons
icons rotating around the vertical axis
in a certain frequency
therefore hopefully inducing SSVEPs
and we claim that this causes less
visual stress or fatigue
and of course you are more flexible in
your design because you can basically
use
every icon you can imagine we wanted to
answer two research questions with our
work
the first was, can spinning icons or
images
be used to evoke ssvps at all and the
second question
was if they can so are they perceived
less visually fatiguing in comparison to
standard or motion-based ssvps
in order to test this we selected four
different icons which are frequently
used in work settings and transferred
them into spinning ssvps
we furthermore designed four SSMVEP so
SSVEPs based on motion and also based
on related work
and we compared both of those stimuli to
a standard checkerboard stimulus
in an application-oriented scenario and
evaluated them based on the
classification accuracy
and subjective perceived visual fatigue
this is how the movement based
stimuli look like and as you can see
they try to
mimic certain UI elements like for
example
maximizing or minimizing button and
arrow for navigation,
settings wheel, x for
closing applications and this is the
control standard checkerboard stimulus
all of those stimuli are oriented on
those two related works mentioned below
for the spinning icons as i said we
decided to use icons which are
frequently used in a working environment
and we chose pdf excel outlook and word
as
icons and as a control we had the
checkerboard spinning as well
i have to mention that excel outlook and
word are of course icons with
property of the microsoft corporation
and this is an independent publication
which is neither affiliated with nor
authorized sponsored or approved by the
microsoft corporation
the experimental setup looked as follows
we had an EEG headset from the
gtec company the gNautilus research
headset with 500 hertz sampling
frequency
we selected 11 electrodes where only one
electrode the Oz was actively used to
measure SSVEP
all the others were used for common
average referencing so to
filter out the background activity and
noise
we designed our stimuli in three
different frequencies
7.5 10 and 13 hertz we had 20
participants where two had to be
excluded afterwards unfortunately due to
problems during the recording
we had 10 stimuli to test three
frequencies each so this resulted in 30
trials per participant
and the classification was done offline
so we did a canonical correlation
analysis based on filter banks
the procedure follows so first the
target stimulus was randomly selected
from all available stimuli
displayed in a random target frequency
for five seconds
participants were asked to remember the
target stimulus
and find it in a grid of all the other
stimuli which was presented afterwards
they had to look at the stimulus until
the screen
changed again and we verified with an
eye tracking system that participants
were actually looking at the correct
stimulus
You might have noticed that the other stimuli
were not flickering in the same frequency as
the targets
frequency or maybe you did not notice
but that was the case
so for example if the target stimulus
was displayed in 10 hertz
all the other stimuli were randomly
chosen to be displayed either in 7.5 or
13 hertz
coming to the results a lot of numbers
and tables first i have to clarify that
we did not really use the classification
accuracy but rather the balanced
accuracy
for our results because the amount of
classifications for the positive
condition and the negative condition is
uneven
with a prevalence of one-third due to
three different target frequencies
and therefore the standard
classification accuracy is a sub-optimal
way of evaluating the classifier
as the negative condition has a
disproportionately higher effect on
accuracy
and as the balance accuracy was used as
the true positive rate plus the true
negative rate divided by two
This resulted for the
positive rate the negative rate and the
balance accuracy
at a level of one-third
classification at chance level because
we had three
different classifications here three
different frequencies in which the
stimuli were presented
so in this table the most important part
is here the average for all the stimuli
of the balanced accuracy you can see our
control stimulus the checkerboard at
the top which produced 7.2 balanced
accuracy
so 72% balance accuracy. All the
motion-based
stimuli were up significantly
above chance level
at least and luckily or
which was a great success or the
spinning
stimuli were all above chance level as
you can see they
scored comparable to our checkerboard
stimulus
and some even better with 75% for the pdf
icon
we evaluated the significance of the
results with the binominal test with a
null hypothesis being
accuracy at chance level so we just
wanted to see if those
balance classification accuracies are
significantly above chance level and the
significance level of 0.05 was used here
what you can see is that for most of our
spinning stimuli actually they were
significantly above chance level
for the motion based it looked a little
worse but still
quite well and what we can see if we
highlight now the
cases in which our spinning stimuli did
not
score significantly above chance level
we can see that it is always 7.5 hertz
which causes problems and not only in
the spinning but also
in the motion based condition and even
in our control condition for the
checkerboard stimulus
so we can more or less conclude that or
discuss that there seems to be a
problem or seems to have been a problem
with the presentation of the 7.5 hertz
stimulus in general
about results concerning visual fatigue
we handed out a questionnaire to the
participants after the experiment where
they had to rate on a scale from one
to five one for extremely fatiguing and
five to not fatiguing at all
how they perceived the stimuli and what
we can see from the
values which are again all presented
here for all participants
that the spinning icons
produced
subjectively less visual fatigue than
the motion based icons
having a look at significance again we
use the sign test where any value smaller
or equal to 3 was counted as failure
and any greater as success with a
significance level of 0.05 again
and we can see that the spinning stimuli
almost all
managed to reject the null hypothesis
but the icon checkerboard did not
Surprisingly also our comparison
our control stimulus managed to do so
and this was
yeah a kind of surprise to us because
our
hypothesis in the beginning was that
those kind of things are annoying to
look at and then in our
comparison it actually seemed to be
that this wasn't the case. Summing up the
work well we presented a novel paradigm
for SSVEP BCIs
based on icons rotating around to
vertical axis
which we call spinning icons or
spinning SSVEPs
we wanted to answer the research
question can spinning icons be used to
evoke SSVEPs
in general or at all and our
classification accuracy is around 70%
and tests on the significance
clearly state yes so you can do it with
this kind of
paradigm about the second research
questions if those spinning icons are
perceived less visually fatiguing in
comparison to standard motion based
SSVEPs we can say yes
it was the case in comparison to
motion-based ssvps
but surprisingly our control stimulus
was perceived equally
or on the same level of visual fatigue
as our spinning icons
so we need some further investigation
into this in the future
speaking about the future, future work
consists for us of course of
testing those spinning ssvps in an
online application scenario
and we would also like to investigate
the effect of different icons
on the spinning SSVEP
that's it from my side thank you very
much and i will be happy
to answer your questions now