Hello! My name is Arnaud Delorme, and this is Part 9 of a series
on independent component analysis applied to EEG time series.
This is a part on automatically detecting ICA component classes.
And this part relies on the plugin designed by Luca Pion-Tonachini.
And this plugin is by default included in EEGLAB,
so if you have installed EEGLAB you should have the plugin automatically.
And first we're going to look a little bit at the website he designed,
because this was part of designing this plugin.
So to access the website you can go to labeling.ucsd.edu.
And on the website you have a link, which is "Just practice"
Where you don't need to log in and you can just practice detecting components.
And so if you go to this menu you'll see a screen like this on your browser,
where you have the scalp topography of the components, you have the activity of the components.
You also have the ERPimage of the components, as we saw for component properties.
In addition, you have the dipole equivalent for the components, either using one dipole or optimizing two dipoles.
And then you have the power spectrum, either zoomed from 3 to 40 Hz and [a large frequency range from] 3 to 80Hz.
And on the bottom you have classes.
So you can select these classes, you can select brain, muscle, eyes, or heart, line noise, and channel noise.
For example, for this one most people selected brain component,
actually all people selected brain component.
So when you click you see your selection and then you see what other people select.
So this can help you figure out what kind of components
you're looking at, because it takes some training to recognize them.
You press next and then you can see another component.
You can see this one is related to eyes,
and sometimes it's not uniform, not not all the experts agree and not all the users agree.
But you can see what's the majority vote for a given [component] scalp topography.
So Luca used this website and 15,000 components were classified.
And then he use these classifications by experts to train a neural network to detect classes of components.
And so that's what we can do when we go to this menu,
the "IC Label" menu, and the first menu [item] is to label components.
There are different [classification] methods, the default [one] is recommended.
So this is going to find different classes of components in particular brain, muscle, eyes, heart,
line noise, channel noise, and others, which were the same [classes] as the button you had on the web interface.
And once you have classified the components -- it just takes a few seconds --
it's going to pop up another window to plot them. And when you press ok you can see the components.
And this is almost the same interface we saw in a previous presentation
where we were classifying the components by hand.
The difference now is that underneath the components you can see a percentage,
and the class which has the highest likelihood of representing the components.
For example, the first one on the right is 71% chance of being a brain component,
and the one on the right is 94.4% chance of being an eye component.
And so for each components the percentage is spread across all the classes.
So it means if you sum the probability for all the classes you get 100%.
And this is conveniently stored in the EEG structure, so you can access that information.
So the next thing you might want to do is to set some threshold.
Let's say you want, in this case, you want to label as
artifact if it has a 90% chance or above to be in the muscle category,
you want to label it as artifact.
If you have a 90 percent chance or above to be in the eye artifact category, you want to label as artifact.
So this is the second menu you would use.
And once you've done that you can plot the components by map, so as if you were rejecting them by hand.
And now you can see they are in red.
So, for example, here this eye blink was detected/selected,
and it's been labeled as artifactual, as if you had done it by hand.
So that means that whenever you want to reject these components it's going to be populating the list automatically.
Or whenever you want to do study/group analysis,
these are going to be removed automatically if you select the right option, of course.
So it is as if you had done it by hand.
So the paper describes in more details the method he used to train the neural network on the
15,000 components using the scalp topography, the power spectrum,
the auto correlation between components.
He also compared [with other algorithms]. There's actually different methods to reject components,
and most of these algorithm -- which are listed here --
there is six listed here -- most of them are available as EEGLAB plugins, so you can also
install them and use them on your data if you want to.
He claims that this method has the most classes of components and also is one of the most efficient.
So thank you for your attention, and I'll see you in the next presentation.