Chapter 08: Plotting ERP images

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Arrow.small.left.gif (MT) Chapter 07: Selecting Data Epochs
Tutorial Outline
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Plotting ERP images

The field of electrophysiological data analysis has been dominated by analysis of 1-dimensional event-related potential (ERP) averages. Various aspects of the individual EEG trials that make up an ERP may produce nearly identical effects. For example, a large peak in an ERP might be produced by a single bad trial, an across-the-board increase in power at the same time point, or a coherence in phase across trials without any noticeable significance within individual trials. In order to better understand the causes of observed ERP effects, EEGLAB allows many different ERP image trial-by-trial views of a set of data epochs.

ERP-image plots are a related, but more general 2-D (values at times-by-epochs) view of the event-related data epochs. ERP-image plots are 2-D transforms of epoched data expressed as 2-D images in which data epochs are first sorted along some relevant dimension (for example, subject reaction time, alpha-phase at stimulus onset, etc.), then (optionally) smoothed (cross adjacent trials) and finally color-coded and imaged. As opposed to the average ERP, which exists in only one form, the number of possible ERP-image plots of a set of single trials is nearly infinite -- the trial data can be sorted and imaged in any order -- corresponding to epochs encountered traveling in any path through the 'space of trials'. However, not all sorting orders will give equal insights into the brain dynamics expressed in the data. It is up to the user to decide which ERP-image plots to study. By default, trials are sorted in the order of appearance in the experiment.

It is also easy to misinterpret or over-interpret an ERP-image plot. For example, using phase-sorting at one frequency (demonstrated below) may blind the user to the presence of other oscillatory phenomena at different frequencies in the same data. Again, it is the responsibility of the user to correctly weight and interpret the evidence that a 2-D ERP-image plot presents, in light of to the hypothesis of interest -- just as it is the user's responsibility to correctly interpret 1-D ERP time series.

Selecting a channel to plot

To plot an ERP image of activity at one data channel in the single trials of our dataset, we must first choose a channel to plot. Let us, for example, choose a channel with high alpha band power (near 10 Hz). Previously in the tutorial we obtained the spectopo.m plot reproduced below.


The plot above shows that alpha band power (e.g., at 10 Hz) is concentrated over the central occipital scalp.

Exploratory Step: Locating Electrodes.

We will use the dataset as it was after the last Key Step, Key Step 8.

To find which electrodes are located in this region, we can simply plot the electrode names and locations by selecting Plot > Channel locations > By name, producing the figure below. We see that electrode POz is the channel at which alpha power is largest. Click on the POz channel label (below) to display its number (27).

Note: It is also possible to plot electrode locations in the spectral graph by entering 'electrodes', 'on' in the lowest text box (Scalp map options) of the interactive pop_spectopo.m window.

Plotting ERP images using pop_erpimage()

Now that we know the number of the channel whose activity we want to study, we can view its activity in single trials in the form of an ERP-image plot.

Exploratory Step: Viewing a Channel ERP.

Select Plot > Channel ERP image . This brings up the pop_erpimage.m window (below). Enter the channel number (27), a trial-smoothing value of 1, and press OK.
I82pop erpimage.jpg
An ERP image is a rectangular colored image in which every horizontal line represents activity occurring in a single experimental trial (or a vertical moving average of adjacent single trials). The figure below (not an ERP image) explains the process of constructing ERP-image plots. Instead of plotting activity in single trials such as left-to-right traces in which potential is encoded by the height of the trace, we color-code their values in left-to-right straight lines, the changing color value indicating the potential value at each time point in the trial. For example, in the following image, three different single-trial epochs (blue traces) would be coded as three different colored lines (below).
By stacking above each other the color-sequence lines for all trials in a dataset, we produce an ERP image. In the standard erpimage.m output figure (below), the trace below the ERP image shows the average of the single-trial activity, i.e. the ERP average of the imaged data epochs. The head plot (top left) containing a red dot indicates the position of the selected channel in the montage.

Note: Both of these plotting features (as well as several others) can be turned off in the pop_erpimage.m pop-up window (above). See check-boxes plot ERP and plot scalp map.
Since activity in single trials contains many variations, it may be useful to smooth the activity (vertically) across neighboring trials using a rectangular (boxcar) moving average.

Exploratory Step: Plotting a Smoothed ERP.

Again call up the pop_erpimage.m interactive window and set the smoothing width to 10 instead of 1. Now (see below) it is easier to see the dominant alpha-band oscillations in single trials.

Note: Because of the large number of available options, parameters from the last call (if any) are recalled as defaults (though optional arguments entered via the text box are not). If you experience a problem with this feature, you may type >>eegh(0) on the Matlab command line to clear the history.
When plotting a large number of trials, it is not necessary to plot each (smoothed) trial as a horizontal line. (The screen and/or printer resolution may be insufficient to display them all). To reduce the imaging delay (and to decrease the saved plot file size), one can decimate some of the (smoothed) ERP-image lines. Entering 4 in the Downsampling box of the pop_erpimage.m window would decimate (reduce) the number of lines in the ERP image by a factor of 4. If the Smoothing width is (in this case) greater than 2*4 = 8, no information will be lost from the smoothed image.

Note: To image our sample dataset, it is not necessary to decimate, since we have relatively few (80) trials.

Sorting trials in ERP images

In the ERP-image figures above, trials were imaged in (bottom-to-top) order of their occurrence during the experiment. It is also possible to sort them in order of any other variable that is coded as an event field belonging to each trial in the dataset. Below, we demonstrate sorting the same trials in order of response time event latency (reaction time).

Exploratory Step: Sorting Trials in an ERP Image.

In the pop_erpimage.m window again, first press the button Epoch-sorting field, and select Latency. Next, press the button Event type, and select rt. In the resulting ERP image, trials will be sorted by the latency of rt events (our sample data has one rt event per epoch. If this were not the case,erpimage.m would only have plotted epochs with rt events). Enter Event time range of -200 800 ms to plot activity immediately following stimulus onsets.
Note: In this and some other interactive pop-windows, holding the mouse cursor over the label above a text-entry box for a few seconds pops up an explanatory comment.

Now, the erpimage.m figure below appears. The curved black line corresponds to the latency time of the event (rt) we are sorting by.
In general, the user can sort on any event field value.

For example, call back the pop_erpimage.m window, press the Epoch-sorting Field button, and select position instead of latency. Remove rt from the Event type box. Finally enter yes under the Rescale box. Press OK. In the resulting erpimage.m plot, trials are sorted by stimulus position (1 or 2, automatically normalized values to fit the post-stimulus space for display). Note that the smoothing width (10) is applied to both the single-trial data and to the sorting variable. This explains the oblique line connecting the low (1) and high (2) sorting variable regions.

Note: One can also enter a Matlab expression to normalize the sorting variable explicitly (see erpimage.m help).
Now, reselect the latency of the rt events as the trial-sorting variable (press the Epoch-sorting field button to select latency and press the Event type button to select rt). Enter no under Rescale (else, reaction times would be automatically normalized).

Use the Align input to re-align the single-trial data based on the sorting variable (here the reaction time) and the change time limits. The latency value given in Align will be used for specifying time 0.

To select the median of the trial-sorting values (here, median reaction time) for specifying the new time 0 (which will be at the response time minus the median reaction time), our convention is to use Inf the Matlab symbol for infinity in this box (as below). If you want to set a different value (for instance, while plotting an ERPimage for one subject, you might want to use the median reaction time you computed for all your subjects), simply enter the value in ms in the Align input box.

Note: Temporal realignment of data epochs, relative to one another, will result in missing data at the lower-left and upper-right corners of the ERP image. The ERP-image function shows these as green (0) and returns these values as NaNs (Matlab not-a-number).
The ERP image figure (below) will be created. Here, the straight vertical line at time about 400 ms indicates the moment of the subject response, and the curving vertical line, the time at which the stimulus was presented in each trial. Compare the figure below with the previous non-aligned, RT-sorted ERP image.

Plotting ERP images with spectral options

Next, we will experiment with sorting trials by their EEG phase value in a specified time/frequency window. Though rt values can be shown in phase-sorted ERP-image figures, we will omit them for simplicity.

Exploratory Step: Sorting Trials in an ERP by Phase Value

To do this, return to the pop_erpimage.m window from the menu. Clear the contents of the Epoch-sorting field', Event type and Align inputs. Then, in the Sort trials by phase section, enter 10 (Hz) under <Frequency and 0(ms) under Center window. Enter -200 800 next to Time limits (ms) to zoom in on the period near stimulus onset, this option appear at the top of the pop window.
I84pop erpimage.jpg
We then obtain the ERP-image figure below.
Note just before the stimulus onset the red oblique stripe: this is produced by phase sorting: the phase (i.e., the latency of the wave peaks) is uniformly distributed across the re-sorted trials.

In this computation, a 3-cycle 10 Hz wavelet was applied to a window in each trial centered at time 0. The width of the wavelet was 300 ms (i.e., three 10-Hz cycles of 100 ms). Therefore, it extended from -150 ms to 150 ms. After the wavelet was applied to each trial, the function sorted the trials in order of the phase values (-pi to pi) and displayed an ERP image of the trials in this (bottom-to-top) order. The dominance of circa 10-Hz activity in the trials, together with the 10-trial smoothing we applied makes the phase coherence between adjacent trials obvious in this view.

We could have applied phase-sorting of trials using any time/frequency window. The results would depend on the strength of the selected frequency in the data, particularly on its degree of momentum (i.e., did the data exhibit long bursts at this frequency), and its phase-locking (or not) to experimental events. Phase-sorted ERP images using different time and frequency windows represent different paths to fly through complex (single-channel) EEG data. (Note: Use keyword 'showwin' to image the time window used in sorting the data for any type of data-based sorting (e.g., by phase, amplitude, or mean value).

To see the phase sorting more clearly, keep the same settings, but this time enter 50 under percent low-amp. trials to ignore. Here, the 50% of trials with smallest 10-Hz (alpha) power in the selected time window will be rejected; only the (40) others (larger-alpha 50%) will be imaged. Here (below), we can better see how the alpha wave seems to resynchronize following the stimulus. Before time 0, alpha phase is more or less random (uniformly distributed) and there is little activity in the average ERP. At about 200 ms, alpha activity seems to (partially) synchronize with the stimulus and an N300 and P400 ERP appears.

Our interpretation (above) of these trials as representing phase synchronization need not be based on visual impression alone. To statistically assess whether alpha activity is partially resynchronized by (i.e., is partly phase-reset by) the stimuli, we need to plot the phase coherence (or phase-locking factor) between the stimulus sequence and the post-stimulus data. This measure, the Inter-Trial Coherence (ITC) our terminology, takes values between 0 and 1. A value of 1 for the time frequency window of interest indicates that alpha phase (in this latency window) is constant in every trial. A value of 0 occurs when the phase values in all trials are uniformly distributed around the unit circle. In practice, values somewhat larger than 0 are expected for any finite number of randomly phase-distributed trials.

Exploratory Step: Inter-Trial Coherence.

To plot the ITC in our ERP-image figure, we choose to enter the following parameters in the pop_erpimage.m window: we omit the Percent low-amp. of Trials to ignore value (or enter ). Under Sort trials by phase>Frequency enter 9 11 and also enter 9 11 in the Inter-Trial Coherence>Frequency box. Enter 0.01 under Signif. level and press OK.

Note that these two entries must be equal (the window actually prevents the user from entering different values). Entering a frequency range instead of one frequency (e.g., 10 as before) tells erpimage.m to find the data frequency with maximum power in the input data (here between 9 and 11 Hz).
I84pop erpimage2.jpg
The following window is created.
I84Coher freq.jpg
Two additional plot panels appear below the ERP panel (uV). The middle panel, labeled ERSP for Event Related Spectral Power, shows mean changes in power across the epochs in dB. The blue region indicates 1% confidence limits according to surrogate data drawn from random windows in the baseline. Here, power at the selected frequency (10.12 Hz) shows no significant variations across the epoch. The number 25.93 dB in the baseline of this panel indicates the absolute baseline power level.

Note: To compare results, it is sometimes useful to set this value manually in the main ERP-image pop-window.

The bottom plot panel shows the event-related Inter-Trial Coherence (ITC), which indexes the degree of phase synchronization of trials relative to stimulus presentation. The value 10.12 Hz here indicates the analysis frequency selected. Phase synchronization becomes stronger than our specified p=0.01 significance cutoff at about 300 ms.

Note: The ITC significance level is typically lower when based on more trials. Moreover, ITC is usually not related to power changes.
Discussion Point: Does the ERP here arise through partial phase synchronization or reset following stimulus onset?

In a 'pure' case of (partial) phase synchronization:

  • EEG power (at the relevant frequencies) remains constant in the post-stimulus interval.
  • The ITC value is significant during the ERP, but less than 1 (complete phase locking).

In our case, the figure (above) shows a significant post-stimulus increase in alpha ITC accompanied by a small (though non-significant) increase in alpha power. In general, an ERP could arise from partial phase synchronization of ongoing activity combined with a stimulus-related increase (or decrease) in EEG power.

It is important not to over interpret the results of phase sorting in ERP-image plots. For example, the following calls from the Matlab command line simulate 256 1-s data epochs using Gaussian white noise, low-pass filters this below (simulated) 12 Hz, and draw the following 10-Hz phase sorted ERP-image plot of the resulting data. The figure appears to identify temporally coherent 10-Hz activity in the (actual) noise. The (middle) amplitude panel below the ERP-image plot shows, however, that amplitude at (simulated) 10 Hz does not change significantly through the (simulated) epochs, and the lowest panel shows that inter-trial coherence is also nowhere significant (as confirmed visually by the straight diagonal 10-Hz wave fronts in the center of the ERP image).

% Simulate 256 1-s epochs with Gaussian noise 
% at 256-Hz sampling rate; lowpass < 12 Hz
>> data = eegfilt(randn(1,256*256),256,0,15);
% Plot ERP image, phase sorted at 10 Hz
>> figure;
>> erpimage(data,zeros(1,256),1:256,'Phase-sorted Noise',1,1,...
 'phasesort',[128 0 10],'srate',256,...
 'coher',[10 10 .01], 'erp','caxis',0.9);

Taking epochs of white noise (as above) and adding a strictly time-locked 'ERP-like' transient to each trial will give a phase-sorted ERP-image plot showing a sigmoidal, not a straight diagonal wavefront signature. How can we differentiate between the two interpretations of the same data (random EEG plus ERP versus partially phase reset EEG)? For simulated one-channel data, there is no way to do so, since both are equally valid ways of looking at the same (simulated) data - no matter how it was created. After all, the simulated data themselves do not retain any impression of how they were created - even if such an impression remains in the mind of the experimenter!

For real data, we must use convergent evidence to bias our interpretation towards one or the other (or both) interpretations. The partial phase resetting model begins with the concept that the physical sources of the EEG (partial synchronized local fields) may ALSO be the sources of or contributors to average-ERP features. This supposition may be strengthened or weakened by examination of the spatial scalp distributions of the ERP features and of the EEG activity. However, here again, a simple test may not suffice since many cortical sources are likely to contribute to both EEG and averaged ERPs recorded at a single electrode (pair). An ERP feature may result from partial phase resetting of only one of the EEG sources, or it may have many contributions including truly 'ERP-like' excursions with fixed latency and polarity across trials, monopolar 'ERP-like' excursions whose latency varies across trials, and/or partial phase resetting of many EEG processes. Detailed spatiotemporal modeling of the collection of single-trial data is required to parcel out these possibilities. For further discussion of the question in the context of an actual data set, see Makeig et al. (2002). In that paper, phase resetting at alpha and theta frequencies was indicated to be the predominant cause of the recorded ERP (at least at the indicated scalp site, POz). How does the ERP in the figure above differ?

The Makeig et al. paper dealt with non-target stimuli, whereas for the sample EEGLAB dataset we used epochs time locked to target stimuli from one subject (same experiment). The phase synchronization might be different for the two types of stimuli. Also, the analysis in the paper was carried out over 15 subjects and thousands of trials, whereas here we analyze only 80 trials from one subject. (The sample data we show here are used for tutorial purposes. We are now preparing a full report on the target responses in these experiments.)

Note: Altogether, there are five trial sorting methods available in erpimage() ->

  • Sort by the sorting variable (default) - Sorts input data trials (epochs) by the sortvar, sorting variable (for example, RT) input for each epoch of the input data.
  • Sort by value (valsort)- Here, trials are sorted in order of their mean value in a given time window. Use this option to sort by ERP size (option not available yet in the interactive window).
  • Sort by amplitude (ampsort)-- Trials are sorted in order of spectral amplitude or power at a specified frequency and time window. Use this option to display, for example, P300 responses sorted by alpha amplitude (option not available yet in the interactive window).
  • Sort by phase (phasesort)-- Trials are sorted in order of spectral phase in a specified time/frequency window. </font>
  • Do not sort (nosort)-- Display input trials in the same order they are input.

Plotting spectral amplitude in single trials and additional options

There are several other erpimage.m options that we will briefly illustrate in the following example. The Image amps entry on the pop_erpimage.m window allows us to image amplitude of the signal (at the frequency of interest) in the single trials, instead of the raw signals themselves. Check this box. The Plot spectrum (minHz maxHz) entry adds a small power spectrum plot to the top right of the figure. Enter 2 50 to specify the frequency limits for this graph.

Change the Epoch-sorting field box back to latency and Event type</font> back to rt. Then enter 500 under Mark times to plot a vertical mark at 500 ms (here for illustrative purpose only). Finally enter -500 1500 under Time limits to zoom in on a specific time window, and -3 3 under Amplitude limits (dB).

I85pop erpimage.jpg

The erpimage.m figure below appears.


In the next tutorial, we show how to use EEGLAB to perform and evaluate ICA decomposition of EEG datasets.

Arrow.small.left.gif (MT) Chapter 07: Selecting Data Epochs
Tutorial Outline
(MT)Chapter09: Decomposing Data Using ICA Arrow.small.right.gif