Hello, my name is Arnaud Delorme
and I'm the main designer of the EEGLAB software
for processing EEG data.
This is part three, source resolved EEG brain dynamics.
Historically, EEG has been used a little bit like reaction time in psychophysics experiments, just as a marker.
And we've discussed that in the first part.
In EEG, you can extract the latency and amplitude of different waves in the average (like the P1, the N1, the P3)
and researchers were not necessarily interested in knowing what was going on inside the brain
they were just using these as markers.
And these markers were relevant for what we are doing.
But, really over the past 15 years, there's been a strong push to bring back EEG to its underlying substrate, which is neuronal activity.
We've talked about that in the previous presentation.
But this presentation is more about trying to find, from the scalp activity, what's going on in the brain.
So we record data from different sites and we want to know what's going on inside the brain.
So here, we have a subject, record [their] data.
This is the raw data for this subject.
Then we select a specific latency (indicated by the red bar).
And this is the activity at different channels at this specific latency.
And each dot represents one electrode.
You have some dots outside of the head model
(here you see the head model with the nose in the front and the ears on the side)
because basically they're below the midline.
So they're still on the head, but to represent them we just put them outside because it's more convenient.
And then you can imagine that [these numbers] represent elevation on the 3D landscape.
And then you can build the surface.
This is similar [to] when they build elevation maps of mountains.
Project this back on the scalp.
And now each dot again is one electrode.
When it's blue it means low potential activity, and when it's red it means high potential activity.
You can also project that on a 3D model of the head of the subject.
So how do we find activity, what's going on in the brain, using this kind of representation?
Well this is the observed potential, or field, at a given latency.
What we want to know is, which source is active?
So the dots here represent activity in the cortex, and the arrow represents a potential (electric field) at that location.
And then there's two problems here.
One is called the forward problem.
So this is from known source activity on the left, trying to reconstruct the scalp channel activity.
And that's a problem that's well posed, and we'll see how to solve it.
Then there's a much harder problem, which is called the inverse problem.
The inverse problem is starting from the scalp channel activity, trying to find the activity of the source in the brain.
So first the forward problem.
The forward problem is well posed because it can always be solved.
And for the forward problem, first we require a head model.
So this is a spherical head model.
On top you have different layers, and so different geometry, and the conductivity between each layers and of each layer.
And on the bottom you can also have a more complex model with more complex geometry.
And then you also need the sensor locations (represented here).
And then you need possible source distribution (represented by the red arrow).
So you need to know where each source can be, what kind of orientation it can take, and the amplitude of these sources.
If you have a fixed set of sources, you use a solver, and you can calculate the activity on the surface of the scalp.
So that's the forward problem.
Since there is no unique solution to the inverse problem (going back from scalp activity to a source level), it is ill posed.
There is no single solution.
There [are] many different configurations of sources that can give rise to the same scalp activity.
So what do we do?
So first we can constrain the model.
So one way to constrain the model is to use single dipoles, or a finite set of dipoles, which are point-like [dipolar sources] inside the brain
and we optimize their location.
We can also use distributed source model and impose constraint on the model.
For instance, the LORETA assumes a smooth distribution.
Then you have also a solution for minimum norm, minimum current, which impose different constraints.
And on top of that, you also have algorithms for solving the inverse problem.
These add additional constraints.
For instance you have MUSIC or Beamforming
which scan the whole brain with single dipole and computer filter at every location, and add additional constraints.
You find the optimal activity of this dipole.
Or, as in EEGLAB, you can perform ICA decomposition (independent component analysis).
And that's one of the main algorithms for which EEGLAB is known.
It uses higher-order statistics to extract projections of sources on the scalp ("simple" maps).
And the identity of ICA solves the first half of the inverse problem.
So what is ICA?
So here we have activity over time of different channels.
We have three frontal channels, and then we have six parietal occipital channels.
And this is a simple task with words presented.
Sometimes the subject is wrong, sometimes they're correct.
And this is the first component.
And each component has an activity, same as every channel, and also has a scalp map or spatial filter associated with it.
This is, for instance, a component that represents blinks.
You can see here that the blink seems to happen just after the subject's response.
It's pretty systematic. After the subject responds, they blink.
You also have many other components, usually as many as channels.
For example, this is ECG.
So this is heart, with scalp topography associated with it.
You can see here, when you have a heartbeat, it's present in the cap channels but it's buried in noise, and ICA was able to extract it.
Here we have EMG.
So muscular contraction here of the temporal muscle
with a typical scalp map here over the temporal area.
(The first two components) The first one we already talked about, the second one is also a blink component.
Then we have our components which represent physiological activity.
For instance, this is theta rhythm, so five oscillations per second
which are clearly visible in this component of your frontal area
so this component is called frontal midline theta rhythm.
We also have here typical components which are activity of our occipital region
with bursts of alpha here, 10 oscillations per second.
What's interesting for this one is like the ERP is plotted underneath
and you can see that the ERP is red, is almost flat.
So these are independent components.
This is the scalp topography for the alpha rhythm.
And the advantage of using these components, and what we mean [when we say] ICA solves the first half of the inverse problem
is that you can compare here on the left the scalp topography of some raw data, or ERP data
and [on the right] the scalp topography of [the] ICA component.
On the left, it's going to be hard to localize or interpret.
You need to use many different sources in different parts of the brain to try to localize this activity.
On the right it's relatively easy to interpret.
Usually we can just put a single dipole in the brain and localize this dipole with very high accuracy for a component.
So this is what we mean by ICA solves the first half of the inverse problem.
And there's a whole lecture on ICA where we'll go deeper into that.
This is just a teaser.
So these are the dipolar scalp projections for components.
Here we have a component projected on the 3D head mesh.
And this is the dipole that would represent the activity of the scalp topography.
This is another one, and this is the other dipole.
When we actually localize them it looks like this.
So you can see the dipoles and their 3D projection on each axis.
This dipole will generate different scalp topographies.
And the idea is that ICA creates spatial filters for functionally independent sources.
So in EEGLAB source reconstruction, you use ICA for separating sources
and then we have a plugin called DIPFIT that performs source localization, either using single dipoles or distributed models.
We also have an extension called NFT.
So when you have the individual subject MRI, you can extract head mesh and do advanced source localization in NFT.
And once you've isolated the source, you can also look at brain dynamics.
So this is the SIFT EEGLAB extension
So that's it for this lecture.
I'll see you in the next lecture.