Hello. My name is Arnaud Delorme,
and this is Part 4: Removing ICA Component Artifact in EEG
from the series on independent component analysis applied to EEG time series.
So here I'm going to explain to you how to remove artifact
component from EEG data.
So on the left here you have EEG data, then you apply the unmixing matrix, you
apply ICA, and then you get a decomposition, which is time course of
components times the scalp topography of the components, and the original data
is equal to the sum of these activations times these scalp topographies.
So the first one here, the top one, for instance, is a blink, and we can see the blink very clearly.
The dotted lines indicate stimuli.
You can see in the second one
shows that P300 components, so some real brain activity. The fourth one likely
represents oscillation, alpha oscillations in the occipital areas.And the last one,
the fourth one, IC4, represents muscle activity, so very high frequencies that we might want to remove.
So in this case we would want to remove both component number one and component number four.
And we do that here by simply zeroing out their activity and then applying the inverse
transformation to get back the data without these artifacts.
So if you remember the previous video in which I showed this formalism in which
you take the ICA activity multiplied by the inverse weight matrix -- which column
represents the scalp topographies -- and then you get back the original data.
What you can simply do, if you want to remove these components, is zero out the
activity of the components, so when you multiply the ICA activity by the scalp
topography, you get back the data without these components.
You could also zero out the columns if you wanted to, and that would be equivalent.
Now let's see some examples, some examples of ICA applied to real data.
So here we have some original EEG data, and we're going
to apply ICA and try to remove this artifact in here.
So in red is the artifact corrected EEG data and here you
can see that the bump in the middle, which represented some eye artifact, is
gone, but the oscillations are not gone.
So it selectively removed this specific artifact.
Here is another example.
So on the left you have the raw data,
this is from 15 subjects here, and then on the right you have after the data artifact removal.
What's very interesting with this data is that actually these were channels here
near the eyes -- these were actually eye channels -- so we are able to process,
actually, cognitive information, cognitive activity on these eye channels after applying ICA.
Here, this is another example, to show that in some cases the
electrodes, which are affected the components -- here we have
two components -- and one is an eye artifact, and one is actually a cognitive component.
So you have this scalp topography of the components and below
that you have what we call the ERP image.
We have another lecture on what is the ERPimage, but to make it short, it is the stacked trials -- so one trial on top of the other -- this way we can see all the trials.
And here they're specifically sorted by the reaction time of the subject.
And then we can see on the left we have a nice component, which activities are related
to reaction time, because the red here is over lapping with the reaction time onset,
the dotted line. And on the right we have a typical blink component.
We can see actually the individual blinks in the data.
And what's very interesting that these two
components -- they're the ICA decomposition --
they have very strong weights on the both the frontal channel.
These components, so these components have weights
on the frontal channel and they highly overlap.
Yet, the artifacts are clearly isolated from the time activity.
So you can have overlapping scalp topographies.
Here, for reference, this is the ERP.
So this is just the average trials for these two components, and we can see,
again, the ERP is very different for these two types of components.
Here's another example of ICA applied to EEG time series.
So here this was a 256 Channel montage and the subjects were moving their
head during the experiment and
when we apply ICA, we had all these components here and
in the back of the head, so we wondered what these were.
And these -- actually -- these components
correspond to the insertion points of the muscle on the back of the head.
So ICA was able to isolate individual muscle groups in this case.
And these components have very high frequency activity, so activity in high
frequencies which are typical of muscle components.
So this was a nice illustration, again, using ICA to isolate muscle activity.
Here's another example, this is a simulation example in
which we took real data and we added artifacts.
So we added different types of artifacts.
We added transient high-frequency, so this is like muscle
activity, and we added them with a special topography.
We added low frequency events, which is like eye blinks,
and again we use a specific topography that's typical of the EEG.
We added signal discontinuities,
which are relatively frequent depending on the amplifier you use -- the EEG amplifier.
We added high frequency noise.
We added some linear drifts, again, which are observed in real EEG data.
And then we added some real EEG data, some very clean EEG data.
This is an example. So here we have all the different types of noise
overlaid on the real EEG data.
The low frequency events, we can see here in, see in color.
We have the linear trends. We have the transient high frequency events.
We have the high noise trials, and we have the discontinuity.
And, again, here we selectively, we only altered some of the trials, and when we try
to have ICA recover which trials were contaminated or tried [to reject artifacts] directly on the raw data.
And that's what's represented here.
So the shaded area show the performance on the raw data without using ICA.
Can we detect these bad trials?
And for these, we use different methods usually.
For instance, the kurtosis, the skewness of the distribution in the data.
And we did the same with after applying ICA.
Can we find out which trials were contaminated?
And what we can see on this curve is especially when we have high noise then
ICA becomes much more powerful than when using it [rejection] on the on the actual data.
So when you have very noisy data, basically these techniques of finding the
bad trials break down, but if you use ICA they don't break down as fast.
So ICA is more efficient at extracting these artifacts from this data.
And we tryed with different ICA algorithms.
Here there are three of them, SOBI, Fast ICA, and we basically found similar
behavior for all these ICA algorithms.
So this is using ICA to either remove the scalp topography of some ICA components,
or to find artifactual trials.
There are alternate solutions.
So here, for example, we are looking at gamma power, so at both 25 Hertz in meditators versus control.
And we found a nice result in which all the meditators, the meditators on average,
show higher gamma power than controls.
The issue with gamma power is that it can very well be muscle -- it's very hard
to assess that it's not muscle even when you use ICA to remove muscle components.
There could be some muscle remaining.
How do we know it's not muscle?
How do we know that the meditators just don't contract their muscles,
and that's why they have higher gamma power?
To do that, what we did is that we applied
the same analysis to the artifactual ICA components.
And that's what's shown here.
So we have the blinks. We also did it on the temporal muscle.
What we found is that we had no difference whatsoever between
meditators and controls when we apply the analysis, the gamma analysis to the artifacts.
So if it was muscle, most likely when we applied the analysis
to the muscle components, we would find the same difference.
That's not what we found here.
So it gives us some confidence that the original difference we saw
was likely due to actual brain activity and not due to muscle activity.
Here's another example.
We did it again here for four different meditation traditions,
we found a nice difference between
the 3 meditation traditions, here on the left, and the controls -- the control is the blue curve.
When we apply the same analysis to the temporal muscle components, we
found no such difference whatsoever, which is a clear indication that
gamma activity in this case was likely not due to muscles.
So I want to thank you for your attention, and I'll see you in the next video.