Chapter 10: Working with ICA components

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Arrow.small.left.gif (MT) Chapter 09: Decomposing Data Using ICA
Tutorial Outline
(MT) Chapter 11: Timefrequency decomposition Arrow.small.right.gif

Contents

Rejecting data epochs by inspection using ICA

To reject data by visual inspection of its ICA component activations, select Tools > Reject data using ICA > Reject by inspection (calling pop_eegplot.m. Note that marked epochs can be kept in memory before being actually rejected (if the option Reject marked trials (checked=yes) below is not set). Thus it is possible to mark trials for rejection and then to come back and update the marked trials. The first option (Add to previously marked rejections (checked=yes)) allows us to include previous trial markings in the scrolling data window or to ignore/discard them. Since it is the first time we have used this function on this dataset, the option to plot previously marked trials won't have any effect. Check the checkbox to reject marked trials, so that the marked trials will be immediately rejected when the scrolling window is closed. Press OK.

Inspectionpop.gif

First, adjust the scale by entering 10 in the scale text edit box (lower right). Now, click on the data epochs you want to mark for rejection. For this exercise, highlight two epochs. You can then deselect the rejected epochs by clicking again on them. Press the Reject button when finished and enter a name for the new dataset (the same dataset minus the rejected epochs), or press the Cancel button to cancel the operation.

Inspectionhighlight.gif


At this point, you may spend some time trying out the advanced rejection functions we developed to select and mark artifactual epochs based on ICA component maps and activations. For directions, see the Data rejection tutorial. Then, after rejecting 'bad' epochs, run ICA decomposition again. Hopefully, by doing this you may obtain a 'cleaner' decomposition.

Important note:what do we mean by a cleaner ICA decomposition? ICA takes all its training data into consideration. When too many types (i.e., scalp distributions) of 'noise' - complex movement artifacts, electrode 'pops', etc -- are left in the training data, these unique/irreplicable data features will 'draw the attention' of ICA, producing a set of component maps including many single-channel or 'noisy'-appearing components. The number of components (degrees of freedom) devoted to decomposition of brain EEG alone will be correspondingly reduced. Therefore, presenting ICA with as much 'clean' EEG data as possible is the best strategy (note that blink and other stereotyped EEG artifacts do not necessarily have to be removed since they are likely to be isolated as single ICA components). Here 'clean' EEG data means data after removing noisy time segments (does not apply to removed ICA components).

For this tutorial, we decide to accept our initial ICA decomposition of our data and proceed to study the nature and behavior(s) of its independent components. First, we review a series of functions whose purpose is to help us determine which components to study and how to study them.

Plotting component spectra and maps

It is of interest to see which components contribute most strongly to which frequencies in the data. To do so, select Plot > Component spectra and maps. This calls pop_spectopo.m. Its first input is the epoch time range to consider, the forth is the percentage of the data to sample at random (smaller percentages speeding the computation, larger percentages being more definitive). Since our EEG dataset is fairly small, we choose to change this value to 100 (= all of the data). We will then visualize which components contribute the most at 10 Hz, entering 10 in the Scalp map frequency text box. We simply scan all components, the default in Components to consider. Press OK.


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The spectopo.m window (below) appears.


Spectopocomps.gif


In the previous window, we plotted the spectra of each component. A more accurate strategy (for technical reasons) is to plot the data signal minus the component activity and estimate the decrease in power in comparison to the original signal at one channel (it is also possible to do it at all channel but it requires to compute the spectrum of the projection of each component at each channel which is computationally intensive). To do so, go back to the previous interactive window, choose explicitly to plot component's contribution at channel 27 (POz) where power appears to be maximum at 10 Hz using the Electrode number to analyze ...: field, uncheck the checkbox [checked] compute component spectra.... Set percent to 100 as before. Finally we will display 6 component maps instead of 5 (default) (note that all component spectra will be shown) and we will set the maximum frequency to be plotted at 30 Hz using the Plotting frequency range option in the bottom panel (below). Press OK when done.

Channelspectraedit1.gif


The spectopo.m figure appears (as below).

I102spectopo.jpg


The following text is displayed

Component 1 percent variance accounted for: 3.07
Component 2 percent variance accounted for: 3.60
Component 3 percent variance accounted for: -0.05
Component 4 percent variance accounted for: 5.97
Component 5 percent variance accounted for: 28.24
Component 6 percent variance accounted for: 6.15
Component 7 percent variance accounted for: 12.68
Component 8 percent variance accounted for: -0.03
Component 9 percent variance accounted for: 5.04
Component 10 percent variance accounted for: 52.08
Component 11 percent variance accounted for: 0.79
....


"Percent variance acounted for" (pvaf) compares the variance of the data MINUS the (back-projected) component to the variance of the whole data. Thus, if one component accounts for all the data, the data minus the component back-projection will be 0, and pvaf will be 100%; If the component has zero variance, it accounts for none of the data and pvaf = 0%. If a component somehow accounts for the NEGATIVE of the data, however, pvaf will be larger than 100% (meaning: "If you remove this component, the data actually get larger, not smaller!"). According to the variance accounted for output above, component 10 accounts for more than 50% of power at 10 Hz for channel POz. (Note: A channel number has to be entered otherwise component contributions are not computed).

Plotting component ERPs

After seeing which components contribute to frequency bands of interest, it is interesting to look at which components contribute the most to the ERP. A first step is to view the component ERPs. To Plot component ERPs, select Plot > Component ERPs > In rectangular array, which calls function pop_plotdata.m. Then press OK.

Rectarrayedit.gif

The plotdata.m window below pops up, showing the average ERP for all 31 components.

I103pop plotdata.jpg

Click on the component-1 trace (above) to plot this trace in new window (as below).

I103single plotdata.jpg

As for electrodes, use menu Plot > Sum/Compare comp. ERPs to plot component ERP differences accross multiple datasets.

Plotting component ERP contributions

To plot the contribution of component ERPs to the data ERP, select Plot > Component ERPs > with component maps, which calls pop_envtopo.m. Simply press OK to plot the 7 components that contribute the most to the average ERP of the dataset. Note artifactual components can be subtracted from the data prior to plot the ERP using the Indices of component to subtract ... edit box.

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In the envtopo.m plot (below), the black thick line indicates the data envelope (i.e. minimum and maximum of all channel at every time point) and the colored show the component ERPs.

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The picture above looks messy, so again call the pop_envtopo.m window and zoom in on time range from 200 ms to 500 ms post-stimulus, as indicated below.

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We can see (below) that near 400 ms component 1 contributes most strongly to the ERP.

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On the command line, the function also returns the percent variance accounted for by each component:

IC4 pvaf: 31.10%
IC2 pvaf: 25.02%
IC3 pvaf: 16.92%
...


Component ERP-image plotting

To plot ERP-image figures for component activations, select Plot > Component ERP image (calling pop_erpimage.m. This function works exactly as the one we used for plotting channel ERP images, but instead of visualizing activity at one electrode, the function here plots the activation of one component. Enter the following parameters in the interactive window to sort trials by phase at 10 Hz and 0 ms, to image reaction time, power and Inter-Trial Coherence (see the ERP-image tutorial for more information).

Componenterpedit.gif


For component 6 (below) we observe in the erpimage.m figure that phase at the analysis frequency (9Hz to 11Hz) is evenly distributed in the time window -300 to 0 ms (as indicated by the bottom trace showing the inter-trial coherence (ITC) or phase-locking factor). This component accounts for much of the EEG power at 10 Hz, but for little if any of the average ERP. Overall, mean power at the analysis frequency does not change across the epoch (middle blue trace) and phase at the analysis frequency is not reset by the stimulus (bottom blue trace). Here again, the red lines show the bootstrap significance limits (for this number of trials).

I105erpimage.jpg


Note: As scale and polarity information is distributed in the ICA decomposition (not lost!) between the projection weights (column of the inverse weight matrix, EEG.icawinv) and rows of the component activations matrix (EEG.icaact), the absolute amplitude and polarity of component activations are meaningless and the activations have no unit of measure (through they are proportional to microvolt). To recover the absolute value and polarity of activity accounted for by one or more components at an electrode, image the back-projection of the component activation(s) at that channel. Go back to the previous ERP-image window, use the same parameters and set Project to channel # to 27. Note that the ERP is reversed in polarity and that absolute unit for power has changed.

I105erpimage2.jpg

In the next tutorial, we show how to use EEGLAB to perform and visualize time/frequency decompositions of channel activities or independent component activations.


Arrow.small.left.gif (MT) Chapter 09: Decomposing Data Using ICA
Tutorial Outline
(MT) Chapter 11: Timefrequency decomposition Arrow.small.right.gif