Today we will continue our three-part lab exercise concerned with EEG and ERP recording and analysis. You will analyze the EEG/ERP data collected during various EEG sessions.

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• Engell, A.D. & McCarthy, G. (2010). Selective Attention Modulates Face-Specific Induced Gamma Oscillations Recorded from Ventral Occipitotemporal Cortex.
• Rossion, Torfs, Jacques, & Liu-Shuang (2015). Fast periodic presentation of natural images reveals a robust face-selective electrophysiological response in the human brain.

Our goal today is to use the skills you developed from last week's lab to analyze EEG/ERP data. There will be less hand-holding in this lab exercise. I want you to explore your data as though you were the first experimenter to ever run the task. Such an experimenter would be searching for differences in the ERP waveforms and their scalp topographies that discriminate their experimental conditions from one another.

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1. You will generate one lab report (LR9) from tonight's work.
   1. Lab Report is due Monday, May 4th @ 7 pm.
2. You should also review (or even start working on) next week's lab so that you can come to class prepared with questions about the EEG project's logistics.
   1. The EEG/ERP Project is due Wednesday, May 13th @ 11:55 pm.

The data for this section of the lab were acquired at the Yale Human Neuroscience Lab.

These data were

• Acquired on a Neuroscan SynAmps2 EEG system
• Sampling rate of 500 Hz (later downsampled to 125 Hz)
• Data was low-pass filtered with a cutoff of 100 Hz
• Digitized with 24-bit resolution

The Contingent Negative Variation is a large, sustained negative deflection that is maximal over central and frontal scalp. It occurs during the interval between a warning stimulus (e.g., 'Ready!') and an imperative stimulus (e.g., 'Respond!'). The CNV was the first ERP discovered that was linked to a cognitive process.

In this experiment, there were three different conditions:

1. Relax! ⇒ 2 sec ⇒ Hold!
   • Subjects knew that they did not have to prepare a response.
2. Ready! ⇒ 2 sec ⇒ Respond!
   • Subjects prepared to respond (Ready!), and then responded (Respond!).
3. Ready! ⇒ 2 sec ⇒ Hold!
   • Subjects prepared to respond (Ready!), but then learned that should withhold their response (Hold!).

You will analyze data from the following participants:

• p01
• p02 (this participant shows the result particularly well)
• p03

• ~/Desktop/class/input/eeg/CNV/p01
• ~/Desktop/class/input/eeg/CNV/p02
• ~/Desktop/class/input/eeg/CNV/p03

I want you to use the ERPLAB technical skills you learned last week to analyze your data set. I will therefore only give you step-by-step instructions for aspects of the analysis that differ from what you've already done. In some cases I've asked you to do the analysis steps in a different order than you did last week (e.g., you will filter the EEG data earlier in the pipeline than you did last week).

Always rename and save the created files using the name “suggested” by EEG/ERPlab, unless I specify otherwise.

You will repeat all steps for each of the three participants. You will then create the grand-averaged ERP across all three participant ERPs. I suggest you complete one participant at a time rather than completing one step at a time across all three participants. I suggest starting with p02 because this participant has the best results.

1. Load the dataset

• ~/Desktop/class/input/eeg/CNV

2. Plot the data

3. Add channel location information

This is not something you did last week.

During data acquisition, the electrodes on the left and right mastoids (M1, M2) were used as the reference. However, we want to use the “average reference” for this analysis. That is, we want to create the average EEG from all 64 EEG electrodes, and then subtract that average from each electrode. Of course, this means that any fluctuations that are in common across all the electrodes will be removed from the data.

1. ERPLAB → EEG Channel operations

• Click the Clear equations button
• Click the Reference assistant button
• In the window that opens, click the average reference button
  • This is a shortcut that ERPLAB has built-in to do the kind of referencing we want
  • Make sure the rest of your window looks like the example below.

• Click OK
• Click RUN

2. The Dataset info window will pop up when the referencing is done.

• Change the name to p01_ref
• Save it as a file

1. Apply an IIR Butterworth bandpass filter to the EEG data with cutoffs of .1 - 30 Hz.

2. Change the dataset name and save the file as p01_ref_bp (bp for “bandpass”).

3. Plot your data again to observe the effects of applying the bandpass filter.

1. See the event codes in CNV task data description above. For this analysis we can use the event names and numbers as our bin names and numbers.

2. Save the Equation List as cnv_list.txt so that you can use it again for the next participants.

3. Save the EVENTLIST as p01_elist.txt.

• Create epochs of -150 to 3000 ms.
• Use -150 to 0 ms as your baseline.

We will use a different artifact detection method than those we used last week.

1. Select the Simple voltage threshold artifact detection and specify the following parameters:

• Test period: 0 1500
• Voltage limits: -100 100
• Channels: 1:64

2. Click ACCEPT

We want to reject the artifacts that were detected in the prior step. So make sure Exclude epochs marked during artifact detection is selected.

Name the ERP p01_cnv_erp and save it to a file of the same name.

A difference wave is a wave that shows the difference between two ERPs. It is nothing more than the difference at each time point; that is, a simple subtraction.

Let's create a difference wave that highlights the effect of preparing to respond. To do so, we can contrast (i.e., difference) the Relax-Hold and Ready-Hold ERPs or the Relax-Hold and Ready-Respond ERPs. Either wave will show the effect of preparing to perform a task because the participant thinks they'll need to perform the task in either of the Ready conditions.

1. ERPLAB → ERP Operations → ERP Bin operations

2. Click Clear equations in the window that opens

3. In the lefthand window type b4 = B3-B1 label Ready_minus_Relax

You can plot your ERP however you think is most informative. Indeed, you should plot it several times with different parameters to find the way that best highlights your results. Some tips:

• The electrodes of interest for the CNV ERP are central and central frontal.
• Plotting from -100 to 2500 ms would be most useful.
• Sometimes the autoscale of the y-axis is not sensitive enough to highlight your results so you might want to manually specify the range.
• If you plot all 64 electrodes it might be hard to see what's going on at any one electrode. If you click on the ERP for a specific electrode, a new window will open zoomed in on that one electrode.
• Make your line colors and line widths visible and easy on the eyes. You can adjust these parameters by clicking the LINE SPEC button in the ERP PLOTTING window.

If you get a warning that there is no channel location info, select Yes to load it now, then click Ok → Ok.

• Plot the topography of the difference wave that you created (bin #4).
• Because we are looking at a slow wave that persists over time, it will be most helpful to plot the mean between two latencies.
  • Set 500 2000 as the Latency to plot. This will show the mean voltage between 500 and 1500 ms post stimulus.
• Be mindful of the color bar scale. The default setting is Max-Min, but this is not very useful because the colors are not symmetric around zero. I suggest you use Abs Max or Custom.
• I urge you to continue to learn how to make cool plots by playing around with the different options.

Rerun all of the above steps for the next two participants.

We will now create a grand average of all three subjects.

It might be helpful to close and then reopen EEGLAB so that you're starting with a blank canvas.

1. Load each of the three participant ERPs. ERPLAB → Load Existing ERPset

• When you're done, click on ERPsets and you should see all three participants listed

2. ERPLAB → Average across ERPsets (Grand Average)

3. Click the button next to From ERPset in the ERPset Menu. This tells ERPLAB that we'll be specifying which ERPsets to average by using those that we've already loaded.

• Enter 1:3 in the box. This tells ERPLAB to use the ERPsets labeled 1 to 3. These are the only ERPsets we have loaded, but this doesn't have to be the case. For instance, you could load the ERPsets of five particpants, but then choose to only average over some subset of them.

4. Click RUN

• Name the file and save it as gavg_cnv_erp

Once you have your grand-average and difference waveforms, plot them just as before. A couple of things to consider:

• How similar is the grand-average to the individual subject averages you already viewed?
• How useful is the difference waveform for isolating the main experimental effects?