Hello. My name is Arnaud Delorme, and this is part 11, the last part of the series
on independent component analysis applied to EEG time series.
This part is about common misconceptions about ICA and some conclusions.
And the first misconception is that ICA always works.
You have EEG data and sometimes whatever
you do you can't get a proper ICA decomposition and by a proper ICA
decomposition, I mean decomposition with scalp maps which are smooth, which
can be represented by equivalent dipole, sometimes they still look like checkerboard.
This dramatically depends on the quality of the data.
If the data is a very low quality, there's a lot of noise in the data, you might end up with
that, but sometimes we don't know why we can't just get a decent decomposition.
So ICA doesn't always work 100%.
The second misconception is ICA can only be used to remove artifact in EEG, and I hope through the series of
presentation I was able to convince you that you can actually look at brain sources using ICA, as well.
And this is one of the slide that I presented where you
can see the contribution of occipital alpha, frontal midline theta, and all ICA separates these components.
A third misconception is that ICA adulterates the phase of the EEG signal.
And this is an idea that was spread by Robert Thatcher,
which is one of the main figures in QEEG analysis and neuro feedback.
And he posted this video on YouTube that got a lot of attention.
And we had a discussion on our mailing list with more than 50 message exchanges with Robert Thatcher.
And we also put up a page with ICA phase distortion, that you can see on
our wiki, which discuss this topic and show that this is misfounded.
Just to summarize in a few words: when you have artifacts like blinks, for example,
if you look at blinks, yes, when you remove the blink ICA is going to change the phase.
This is actually a good thing, because you remove the artifacts.
So I invite you to go and look at this discussion in more detail, if you're interested.
There's a lot of experts contributing to this topic.
The third, another misconception is that
ICA should not be used for connectivity analysis of underlying brain sources.
And this is more grounded in science than the previous one.
And it's based on the idea that ICA separates sources based
on their independence, so why would you want to study the connectivity between
this independent component, these components which are independent.
And the idea here is that ICA is a maximum projection algorithm, so it's gonna find the sources which are
maximally independent, but there might be some residual dependence between the components.
So this was actually studied in detail by Stefan Haufe and collaborators in a paper in which they had simulated
sources and they use different algorithms to separate the sources and calculate the connectivity.
And in this simulation paper ICA didn't necessarily
fare well, you can see at the bottom here it has the highest error of all the methods
tested, but some other variants of ICA did fare well that they tested in this paper.
Also, this was based on one specific ICA algorithm and there are now
more powerful algorithms, such as Pekar, that can obtain source which are much
more independent and much more, you know, and it might work better in that case.
The field right now is geared towards using distributed source modeling and
then oscillating the brain in two different brain areas and calculating the
connectivity between these region.
But this has its own problem, because ICA
is data driven, so it's gonna find the source in the brain, which are specific
to the subjects, and this is not data driven, so this might not work for all the subjects.
We've published, actually, an article on the topic of using ICA to calculate connectivity analysis.
And here this is using seven brain different areas
and Granger causality between this area and show that ICA
does a good job in this specific case, and we obtained some relevant connectivity analysis.
The main issue with ICA and connectivity has to do with
aggregating the data across subjects, because as we saw in one of the previous
presentations some subjects might have two components, but you recall the
subjects the next day and they might not have the same components, some components might be missing.
So how do you account for this missing data?
And so that's the main challenge we're looking at
connectivity analysis using ICA across groups of subjects, is how do you account for the missing data.
And so there's been some effort from our lab in that direction.
One of the, one of them is called group shift, and that's a plug in EEGLAB.
That basically is a solution to aggregate components across subjects.
There's more effort based on Bayesian analysis with Wes Thompson at UCSD, and we hope it's going to move
forward so we can use this solution and compare them with other types of solution for connectivity analysis.
So finally this is a list of paper we've published on the
topic, and there's, the links are in the description.
And I want to acknowledge all the people who've contributed to to this project.
In particular, Ramon Martinez, here on the left, was one of EEGLAB programmers.
Makoto, which you might have seen on the EEGLAB list, because he answers lots of the questions.
Luca, who develop IC label, which I talked about.
Scott Makeig, of course, which is director of the lab, and Tzyy Ping Jung next to him, which is the co-director of the lab.
And, also, Fiorenzo Artoni here on the right, and Fiorenzo contributed to RADICA, bootstrap ICA.
And Jason Palmer, who developed Amica.
And I also want to thank you for your attention.
One of the questions I got in the comments, and from all my colleagues,
was why did I always have the same shirt on all this presentations?
So I invite you to look at the following video to see if I have been able to change my shirt.
Thank you.