Hello my name is Arnaud Delorme
and I'm the main developer and software architect of the EEGLAB software
for processing electroencephalographic data.
So in this series of videos, I'm going to show you why we developed EEGLAB
what's the theory behind EEG, and some basics about using EEGLAB.
In this first part I'm going to explain the reason why we developed EEGLAB
and historical ERP (event related potential) approach and its limitation.
Human electrophysiology was discovered 1926 by Hans Berger, who was a psychiatrist in Germany.
When he was about 20 years old, he had an experience where he fell off his horse.
His sister had an intuition that something happened to him, and she asked their father to send a telegram.
This had a very strong influence on him.
He thought there was some experience of telepathy, and that's what pushed him to record brainwaves and see what's happening in the brain.
So this is an example of his early recordings.
Some other EEG milestones in the 20th century were the first analysis of EEG (1938)
and in 1962, the first computer for evoked potential averaging, or CAT.
This really was the onset of the event-related potential averaging, this era.
What happened with this computer is that it wasn't a digital it was a digital computer but he was recording EEG wasn't recording the raw data.
He was just averaging on the fly, and separate trials.
So people didn't have access to the raw data.
They couldn't do spectral analysis, they couldn't do any kind of advanced analysis.
They could only look at event-related potential averages, and we could only try to interpret these.
This was the onset of the whole era of event related potential averaging
where people try to interpret the peaks of the ERP very often in the same way that they would interpret reaction time, as an additional measure.
So at that stage people are not necessarily interested in what is happening in the brain
they just used this as an additional measure of behavior.
Then in 1979, there was the first source localization of EEG and MEG
and this was the start of the era of functional EEG brain imaging.
In 1995, the first multi-source EEG filtering by ICA to remove artifacts and separate sources.
And in 2009 the first commercial dry electrode device.
So this is just a short history of EEG over the years.
We're really going to focus on this first era, on which most of the research was done until recently
and see how we can go beyond event-related potential averages.
So first, if you don't know what event potentials are, I'm going to try to explain that.
In this slide, you have a cap.
You have many electrodes on the cap that record potential over time.
And then you present stimuli to your subjects.
So here you have two types of stimuli, the blue ones and the red ones.
And these can be sounds, these can be images of different types
so that's why here we have blue and red, different types of stimuli.
Then once you have acquired the data, you can do offline processing.
So a type of offline processing you would do are event-related potential averages.
So we take one electrode (for instance here, the last one)
and then you slice the signal around the stimulus of interest
(so either the blue or the red one).
Then you remove the baseline (so that's the part that's just preceding the series)
and average all the blue ones and all the red ones for one electrode of interest
(so for instance here, the last one).
So this is the period where you would remove the baseline and that's what's represented here.
So then you have an average for both the blue and the red.
The average might be different, so you may try to interpret the difference
especially the posterior peak that might be different.
Here is another example.
So [these are] different trials, trial 1, 2, 3, up to n.
You just average the single trials, and try to interpret the latency and the height of the peaks.
So this is the historical average ERP approach.
These are typical peaks which are usually observed.
You can notice that the scale on the Y axis is actually inverted.
That's the standard format for looking at ERP, although some people prefer it in the opposite direction.
Typically you have peaks which are named P1, N1, P2, N2, P3.
P1 is the positivity at 100ms, N1 is the negativity at about 100ms.
P2 is positivity at about 200ms, N2 is negativity at about 200 ms.
P3 is positivity at about 300ms, although here we see most often the P3 peaks at about 400ms.
And you would look at this for different types of stimuli.
So a typical experiment, you'll have a frequent stimulus and a rare stimulus.
So if it's sound, it could be
beep
boop
beep
beep.
The rare stimulus tends to elicit what's called the P3 (P300, in red here).
You also have differences potentially in the other ERP, the N100 (N1).
Then you can also look at the difference between the two curves.
And when you look at the difference, there [are] other peaks which are interpreted in literature.
For instance, here when we look at the difference we have MMN mismatch negativity for audio sound.
So these are typical ERP analyses.
And the assumption is that the EEG data is equal to an average plus some background noise, and you just have to remove the background noise.
[EEG = ERP + some EEG noise]
But this (?) decomposition is only true if the average appears in every single trial
and the background is not perturbed in any way by the event which is presented.
And so this is what EEGLAB is about, is this assumption true or is it not true
and I'll show you in the next few slides that it's not always true
and most often it's not true.
So the first tool here, one of the tools we developed in EEGLAB, is called ERP image
and that's a way to look at the single trials.
So this is one trial, and you can see the potential (the EEG activity) for one electrode
and this can be color-coded.
Blue is going to be negative potential and red is going to be positive potential.
We can do that for several trials
and then you can stack them up to create what we call an ERP image.
So by default they're sorted by time on task (first trial, second trial, etc.)
but you can also find different types of sorting.
So for instance, these are about 400 trials, unsorted
and the black trace here represents the reaction time of the subject.
Now what we can do is we can sort the trials by the reaction of the subject
(represented here).
What we can also do to see the evoked activity better is that we can smooth the trial across the Y dimension
(represented here).
And then we can see patterns emerging.
And in all the cases, you see in the bottom of each plot
the event-related potential is exactly the same, we just reordered the trials.
But we can see much more information in the sorted and smoothed ERP image than we can see in the ERP.
So how can you use that information?
In this example, this was a visual task, and these [are] the distractor trials for the visual task.
Here the trials are sorted not by reaction time, they're sorted by the phase of alpha.
And here, these trials are only the trials with the highest alpha power.
Usually when you do standard event-related potential analysis you remove all the trials with alpha because you consider alpha is strong background noise
so you don't want your data to be contaminated with these trials.
So these are trials with highest alpha, sorted by alpha phase
and these are the trial with lowest alpha, sorted by alpha phase
and according to the standard ERP hypothesis, alpha is background noise.
Whether you pick up the trials with the highest or the lowest alpha, it shouldn't influence the ERP.
But what we see here is that the ERP is actually very different for trials with high alpha versus low alpha.
So the assumption that the background noise activity is independent of the stimulus, not going to be affected by the stimulus, in this case is not verified.
Here is another example of what you can do using this kind of technique.
This is the same task.
And here, we looked at the targets, and here is the ERP for one frontal channel.
It's actually below the eye, so we spend a lot of time removing artifacts.
We're looking at the ERP of this channel (in red), and we're looking at the ERP image.
So this is the ERP image, sorted by reaction time (dotted line represents RT).
We can also align trials based on the response of the subject instead of the stimulus
(represented here).
So this is the same data, just aligned differently on the response.
Then we looked at the slope of the activity preceding the response
and what we observed is that the slope was proportional to the reaction time.
So again, if you just look at the average, you have to look at the single trial
so you have to look beyond the even-related potential average.
Another way to look beyond event-related potential average is to look at spectral power changes.
So this is a representation of a time frequency decomposition
and we define the baseline as having zero activity.
And you can see that after stimulus presentation, you have an increase in brain wave amplitude
here in this case in the theta for about 4-6 Hz time frequency range
and then again, after about 600ms, a decrease in brain wave amplitude.
So again that's not visible in the ERP that's another way and data here this is here's a more concrete example this was a task where we asked
people to count their breath and when everybody lose the count they have to press the button this
is zero is the button press and before zero is when they last account and after zero is been
when they're regaining concentration is starting to do the task again and again we see a very
clear pattern here of inversion between alpha and theta and this type of analysis you can when you
just look at ERP you can't see any of its brain dynamics here another one another representation
services time frequency decomposition and then in single trials we look here at the correlation of
the brain wave amplitude with reaction time and then we can see that we have a strong correlation
between amplitude and reaction time that's actually negative correlation and this correlation
is preserved even when we remove the ERP so we go in every single trial and we do what's called
regress out the ERP we find that the airpipe at best match in amplitude the single trial and we
remove that and we can see that even in that case we have the activity is still present so that what
we were observing was indeed depending on the event related potential average so this sir this
is the reason why we develop eg lab is to really apply more advanced signal processing methods to
EEG signal and go beyond the ERP model you can see here in this plot the number of citation for
EEG ERP and brain oscillation and the evolution over the year so in in the 1960s you had the
first cat system which I mentioned previously and the rise of the European and and in about the 80s
you had even related this synchronization so that spectral analysis of EEG then in the 1990s EEG ICA
and ERSP (event-related spectral perturbations) as the plot I shall be folded the spectral
plots and EEGLAB was created in the year 2000 and contributed to this change.
So in the next series of videos, I'm going to show you where the EEG signal comes from and what you can do with EEGLAB.