Hello. My name is Arnaud Delorme, and this is Part 8 of a series on independent
component analysis applied to EEG time series.
This part is about removing artifactual components in EEGLAB.
And so we have run ICA, so now the next step is to look at the decomposition,
and for this you have the next menu on the list, which is inspect/label components by maps,
depending on the version of EEGLAB you have. This is for the newer versions,
the older versions that have a slightly different menus, but this one is already in there.
So you click on this menu and this is what pops up, so a series of components.
And here we have different components, so we're going to go in
detail looking at these different components.
First we have eye components, and second we have muscle components, we also have cardiac components,
and bad-channel components, and finally we have brain components.
So we look at all of them, except the cardiac components.
And so first, let's look at the eye components.
So the scalp topography alone, here, is not enough
to really figure out what type of component it is.
You have to look at the details.
So when you click on the button, here, for this component for example
this is a typical eye-blink component.
We have the spectrum that's 1/F curve, and then for the component activity,
you see the ERPimage -- we have little dots and this represents individual blinks.
So we can clearly see that this is an eye-blink component.
If we don't want this component to be retained then
we have the little accept button on the bottom which is green, and we can click on it
and then it toggles to reject and then this component is labeled for rejection.
Here is another eye component.
This one is lateral eye movements. Again, we can see -- not as clearly as for the blinks -- but we can see in individual trials
some deflection and this corresponds to eyes going left and right.
We can also look at the component activity, here,
by going into the plot menu of EEGLAB.
And you can see that over [a duration of] half a second you have these deflections, which represents the lateral movements.
Then we have the muscle [components].
So here competent 10 and 12,
and when we look at their properties we can see the spectrum is very different.
See the spectrum before was of 1/F, and
this spectrum [by contrast] has a lot of power in high frequencies.
We can also look at the ERPimage, and we see
it's very noisy, and very often these components are only transiently active
during the experiments, so they might only have a set of trial that's very
noisy and then the data goes back to normal.
That's a little bit the case for the component on the right where you can see
most of the noise is in the first trials from zero to fifty.
Then we have the single channel components.
So these are components which represent bad channels.
So the channel went bad at some points and it's not in all the trials, only a subset of trials.
I would usually personally not reject these components and I would just remove the
channels or remove the trials that contain these artifacts. Because these channels
might still contain some activity that's related to the experiment,
so I would prefer to be very conservative and not necessarily remove this
channel, rather remove the data corresponding to this channel.
These are the location of the channel and the ERPimage.
And when we look at the component activation -- when we scroll
component activities -- we can see the two types of components -- here are the muscle [components].
So you see this is a very high frequency activity and we can see the single channel, we can see
trials where the activity goes wild, and then on some trials there's no artifacts.
So the scrolling the component activity is also
useful in figuring out what type of component it is.
And then we have brain components.
So I have already talked about brain components at length.
This one is more occipital alpha, we see the peak at 10Hz.
So I'm not going to develop too much further on brain components.
So you've run ICA and there is the next menu, which is
classify components, so you, there is a method for automatic classification,
that's one of the method, and this is going to be in the next video.
But once you have found your [artifactual] components you might want to remove them from your data.
And this is the next menu [item] "remove components from data."
You would only do this
if you're not going to do group analysis, because when you do group analysis there is also
a method that would automatically remove these components.
So it's actually better to just label them and then go to group analysis,
if you're going to do group analysis (study level analysis).
But if you just want to see how the data looks like
when you remove the components you select this menu [item] "remove components from data",
and then this interface pops up and then there's numbers automatically populated here.
We have two. So these are all the components in which we toggle the button
from "accept" to "reject." They automatically show up here and we can decide to remove them.
So we press the "ok" button, and then we have ways to check the data.
So first we have a button that is "plot ERPs", and these are the ERPs
before and after component removal.
So in red, before, and in blue, after component removal.
You can see that sometimes for some channels the ERP, like
the one that is circled, is dramatically affected. This is, for example, [due to] eye channels,
because in this specific experiments the subject might have had blinks just after the stimulus.
So the blinks dramatically contribute to the ERP,
but that might not be representing a cognitive ERP, so it's fine to remove it.
You can also "plot single trial."
So you can see here it's inverted [compared to the previous figure], so in blue it's before rejection and in red it is after rejection.
This has been fixed in the most recent version of EEGLAB.
And here you can see that how ICA removed the blink and you can see the residual information.
So that's very useful to see if ICA did a good job in removing the blink,
and you can see the alpha burst that is still here and this kind of [similar] information.
Once you're happy with the components that were rejected,
you can [press] "accept" and these components would actually be removed from the data.
So thank you for your attention, and I'll see you in the next video.