Hello. My name is Arnaud Delorme and this is Part 10 of a series of presentations
on independent component analysis applied to EEG time series.
This part is about looking at brain components.
And so, the simplest way to look at brain components is to go to EEGLAB menu,
components ERP, and plot components ERPs in rectangular arrays.
You can select the components you want to plot.
And this is the plot you get where you can see the ERP of each of the individual components.
So that's already useful, but there's actually more powerful plotting menus in EEGLAB to do that.
And so, first let's backtrack a little bit so I can present you these menus.
Let's go back to channel ERP.
So channel ERP, you have a menu to plot channel ERP with scalp topographies, and you select the time
range you want to plot, you select the scalp topography latency, and this is typically what you get.
You get the ERP for each channel, here, represented by a different color, and then
you get the scalp topography at the latency you've selected.
And so, here an important distinction is that EEG, the peaks here, are sometimes
called ERP components so not to be confused with ICA components.
And so, now we're going to define what we call the data envelope.
So we have the data, that's all the channels, so the trace for each of
the channels, and what we call the envelope is the mean and the max trace at each latency.
So we can define that for channels, but we can also define that for components.
So, here, we have one component, and we have the data envelope in black here, and we have the
IC topography and one components we're just plotting one component here.
And then we have the back projected activity of
these components into the channel space and each channel here will have a different trace.
All the trace will be proportional, but they'll be of different amplitude.
And what we call, we call the IC envelope the envelope of this trace of components.
So you can see that here, for example, this component accounts for
most of the activity of 150 milliseconds, but not the activity at other latency.
And we can calculate how much it contributes within a given time window to the ERP.
This is what we call the percentage variance accounted for, so in this case it's 37%.
And the variance PVAF is the variance of scalp EEG accounted for by this component.
And so let's look at more components.
So to do that you go to this menu plots, component ERP,
with component maps, and you'll have a time window again.
You'll have the time window you're interested in for the component contribution,
you'll have the number of components you want to plot.
And you also have an additional option here to ignore
some components, both from the ERP and for the contribution to the ERP.
So these are the components you might have labeled as artifact.
And so that's useful, because even though you might not have removed
these components you can still ignore them here at this point and they will be
removed from the ERP and also not contribute.
So this is what's being plotted here.
And then we can see that two components pop out here.
For example, component number three seems to account for most of the ERP peak the first three
ones before 300-millisecond, both the positive and the negative ones.
What component 15 needs to account for most of the late ERP after 300 milliseconds.
So this is very useful to look at individual subject data and then
guide your analysis at the group level.
You would look at a couple of subjects
and find some regularities, and then you can look at the group level and cluster
components and look at cluster of component contribution to the ERP.
You can also plot the difference envelope between two conditions.
So that's the second menu here, second set menu.
And there is an interface where you can select options, but basically that's what's being plotted.
And now it plots the difference between two conditions and how different
components contribute to the difference within a specific latency range.
So this was for ERP.
Sometimes you don't have stimulus in your data, so sometimes
you just want to know the contribution to the spectrum.
How much do component contribute to 10hz, for example?
So you'd use this menu plot, competent spectra and map.
And you can enter different options.
You can enter the frequency you're interested in.
I want to know the contribution 10hz, for example, and
the component you want to plot and many different options.
And so this is what's being plotted.
You can see the scalp topography so this is the EEG power spectrum at 10hz, and
you can see which component contribute the most to the 10hz activity.
These scalp maps here don't work exactly like the scalp map for ERP,
because they're squared, because we're looking at power, here, so we square the
scalp topography of the components.
Let's look at different frequency.
Let's look at 6hz, for example.
And now you have the contribution which
component contribute the most to 6hz, and these are very different
components in this case from tall midline theta ribbon.
So thank you for your attention, and I'll see you in the next video.