Hello, my name is Arnaud Delorme
and I'm the main software architect of the EEGLAB software.
This is part 5, about EEG pre-processing in EEGLAB
and this part is about examining your data and looking at artifacts.
So we are now at this step, examining raw data.
There [are] two ways to look at raw data in EEGLAB.
There is this menu, "Channel data (scroll)"
and there is this other menu, "Tools -> Reject data by eye"
and both menus actually have the same functions.
You can do the same thing with both menus.
When you click on these menus, then you can see your data.
So we're going to look a little bit at this screen.
So first, you have "Settings."
This is where you set the times, the number of channels you want to see, whether you want to display events or not.
You have this edit box here, where you can see where the beginning of the data is pointing to.
So here at time 0 is the beginning.
It can also be when you're visualizing data epochs,
what will be, what is the first epoch that's being seen?
These buttons are for scrolling back and forth.
This is the button for scaling, so you can scale the EEG up and down
and here is the scale.
And then you have event markers.
So in this case I have two types of events.
I have square and RT, which means reaction time.
So square is the stimulus, and RT is the reaction time.
And here we can see some alpha waves very clearly in this subject.
Now what kind of artifacts should you look for?
First, why look for artifacts?
Well, the amplitude of artifacts, such as eye movements, is often larger than the amplitude of brain data
which [could] potentially decrease signal-noise ratio, and bias data analysis and potential results when you process your data.
This is EEG data, so this is background EEG data, and these are blinks
so you can see the amplitude of a blink is an order of magnitude higher than the underlying brain data.
There [are] many types of artifacts, here I'm just representing a couple.
For instance, you can have high-frequency artifacts.
These can be muscle, especially over temporal electrodes.
You can have low frequency, for instance, eye movements
or even when the eyes [are] closed you can have eye-rolling.
You can have a discontinuity in the signal, as we can see here.
These jumps are just artifacts of the amplifier.
You can have high noise. That's not muscle, it's just electrical noise.
You can also have a linear drift in your data.
The other thing you might want to do is look for bad channels.
Here, we can see this channel is probably a bad channel.
So it is very important you look at your data, that's one of the key steps.
There is no universal method for cleaning EEG data, so you have to look at your data
and familiarize [yourself with] what kind of artifacts some methods remove
and what are the true artifacts you would want to remove?
So here I'll do a simple live demo
again using the same continuous dataset in the sample_data folder.
So "File -> Load existing dataset."
And the sample_data folder of the EEGLAB.
That's the one.
I'm going to use this menu, "Plot -> Channel data (scroll)."
So we can move forward.
We can change the scale here
so here we see nice alpha rhythm.
There's not much artifact.
There's one artifact here and a strong discontinuity in this signal, and here another one
but it's been cleaned overall and made available to people.
In the next part, we'll talk about removing bad channels.