Hello. My name is Arnaud Delorme, and this is a series
on independent component analysis applied to EEG time series.
This is Part 6 on the Reproducibility of ICA Decompositions.
If we run ICA several times on very similar data, is it going to give the same solution?
So to test that idea, we run ICA several times.
So we had 2 subjects and each subject came eleven times to the lab.
So we have eleven times about an hour of data for each subject.
So we wanted to know if we run ICA on the data on the different days, do we get
similar decompositions or do we get different decompositions?
How reproducible is it across days?
So we preprocessed the data, filtered the data and
then run InfoMax ICA on each of the data sets.
And then we use a special technique to find the components which were common across datasets.
For this we used the Corrmap plugin of EEGLAB, which was developped by Stefan Debener's group.
So the picture over there shows how this plugin works.
It looks complex in the picture, it's actually just looking at correlation of scalp topographies.
And I'm going to show you examples of components we got on one of the subjects.
So here, for example -- so we had eleven sessions --
so here, we got the components for the eleven sessions.
And here, you can see that
the top scalp topography is the average,
and then we have the scalp topography on each of the sessions.
And some of them are inverted and as we saw in one of the previous presentation,
it doesn't really matter because if you multiply by minus 1, the scalp topography and multiply
by minus 1 the ICA activity, it cancels out.
So it doesn't matter that they are inverted, it just means the ICA activity is also inverted.
What's interesting is that the power spectrum for two of these components
was very different and it might be for this specific subject that we had several components,
splitting the activity from this brain region.
The cluster was relatively tight.
Here is another example.
These are the blinks.
So here you see very similar components in the eleven sessions recorded on different days from this subject.
The power spectrum is very similar at the localization as well.
Here, this is the left mu rhythm. This is a rhythm that's called mu because
-- in the power spectrum -- you get this letter M.
And again, most of the components are similar,
but we have one that's a bit of an outlier with lower power.
Here is another example, this is now the right mu rhythm.
And now we only have seven out of eleven sessions, so we couldn't find it in all the sessions
and it's unclear why they can be found in some sessions and some others.
It might be that in some sessions more artifact dominate so ICA can't find these components.
And here is another one.
This is left alpha rhythm.
And we can see, we could find this one in six of the eleven sessions.
And finally, here, we have a nice central, very sharp alpha peak, but we
could only find this one in four of the sessions.
So to access the quality of these clusters, we work with a researcher, who's name is Florenzo Artoni
and he developed a method that is called bootstrap ICA.
So just take the data, it's always the same data from the same subject, so just take a subsample
of the data and you run ICA multiple times.
And again, this is for the InfoMax ICA, although this has also been tried for the Amica decomposition.
And when you do this, for instance you run one thousand times ICA,
which components are stable and which components are not stable.
And again, for InfoMax this is not a deterministic algorithm in the sense that every time you
run it, you start from a different weight metric: the weight metric is randomized.
If you use the same randomization you're going to get the same result.
But usually you use different randomization of the weight metric, so every time you run InfoMax,
it's going to give you slightly different results.
So this is what's shown here, and this is a 2D projection
of the components in multi-dimensional space.
And we can see different clusters of components, right mu rhythm, left mu rhythm, the saccades, etc.
So let's look at some of these.
So this is for, example, IC9, and for each component we have the quality metric, so the quality is the cluster,
it is the tightness of the cluster in the previous image.
How tight were these data points?
And then we have the dipolarity which we talked about in the previous video.
And we have the variance, how much variance of the data this explains.
So we see, for instance, for this component it explains that about 4% variance of the
data, dipole fit is about 99%, the residual variance is less than 1% and the quality is high.
It is very tightly clustered across the multiple runs.
Here are some other ones.
And we can see, for example, IC3 has some kind of bimodal distribution, so the dipolarity
is not always at the highest at about 95%.
For instance, IC10 which likely represent line noise artifact:
even though it's very tightly clustered, (98%), so you can see it's not very dipolar
but it explains a lot of the data, about 30%.
And then we have many other components, some of lower quality index like 86%, 92%.
So this shows us the different components we can get,
and if we can get some components which are reliable or not.
And now we come to this slide about the reliability criteria.
And when we do group analysis we usually set a threshold at 15% for the residual variance.
We set any components which doesn't have an equivalent dipole with less than 15%, let's not consider it.
And here, in this paper we found some rational for this number,
because we found three classes of components.
We have components with very high dipolarity, so above 85%, here it's 100% minus the residual variance.
And then we have class two, which have a high quality index, but various levels of dipolarity.
So this is like the blue components I showed you in the previous slide.
And we have class three, which are all the components, which have low quality index.
What's very interesting here is that when they have low quality index, that's the red box here,
you don't have any components with low quality index which are not at least 85% dipolar.
So if you are 85% dipolar, you usually have a relatively high quality index.
And they were 14 datasets, so you can see the classes varies between datasets.
Some datasets have more of one class versus the others.
So this is the end of this presentation, and I'll see you in the next one.