Chapter 09: Decomposing Data Using ICA
Independent Component Analysis of EEG data
Decomposing data by ICA (or any linear decomposition method, including PCA and its derivatives) involves a linear change of basis from data collected at single scalp channels to a spatially transformed "virtual channel" basis. That is, instead of a collection of simultaneously recorded single-channel data records, the data are transformed to a collection of simultaneously recorded outputs of spatial filters applied to the whole multi-channel data. These spatial filters may be designed in many ways for many purposes.
In the original scalp channel data, each row of the data recording matrix represents the time course of summed in voltage differences between source projections to one data channel and one or more reference channels (thus itself constituting a linear spatial filter). After ICA decomposition, each row of the data activation matrix gives the time course of the activity of one component process spatially filtered from the channel data.
In the case of ICA decomposition, the independent component filters are chosen to produce the maximally temporally independent signals available in the channel data. These are, in effect, information sources in the data whose mixtures, via volume conduction, have been recorded at the scalp channels. The mixing process (for EEG, by volume conduction) is passive, linear, and adds no information to the data. On the contrary, it mixes and obscures the functionally distinct and independent source contributions.
These information sources may represent synchronous or partialy synchronous activity within one (or possibly more) cortical patch(es), else activity from non-cortical sources (e.g., potentials induced by eyeball movements or produced by single muscle activity, line noise, etc.). The following example, from Onton and Makeig (2006), shows the diversity of source information typically contained in EEG data, and the striking ability of ICA to separate out these activities from the recorded channel mixtures.
View full-size version of this data image. Fifteen seconds of EEG data at 9 (of 100) scalp channels (top panel) with activities of 9 (of 100) independent components (ICs, bottom panel). While nearby electrodes (upper panel) record highly similar mixtures of brain and non-brain activities, ICA component activities (lower panel) are temporally distinct (i.e. maximally independent over time), even when their scalp maps are overlapping. Compare, for example, IC1 and IC3, accounting for different phases of eye blink artifacts produced by this subject after each visual letter presentation (grey background) and ensuing auditory performance feedback signal (colored lines). Compare, also, IC4 and IC7, which account for overlapping frontal (4-8 Hz) theta band activities appearing during a stretch of correct performance (seconds 7 through 15). Typical ECG and EMG artifact ICs are also shown, as well as overlapping posterior (8-12 Hz) alpha band bursts that appear when the subject waits for the next letter presentation (white background) For comparison, the repeated average visual evoked response of a bilateral occipital IC process (IC5) is shown (in red) on the same (relative) scale. Clearly the unaveraged activity dynamics of this IC process are not well summarized by its averaged response, a dramatic illustration of the independence of phase-locked and phase-incoherent activity.
Running ICA decompositions
KEY STEP 9: Calculate ICA Components
- To compute ICA components of a dataset of EEG epochs (or of a continuous EEGLAB dataset), select Tools > Run ICA. This calls the function pop_runica.m. To test this function, simply press OK.
We detail each entry of this GUI in detail below.
ICA Algorithms: Note (above) that EEGLAB allows users to try different ICA decomposition algorithms. Only runica, which calls runica.m and jader which calls the function jader.m (from Jean-Francois Cardoso) are a part of the default EEGLAB distribution. To use the fastica algorithm (Hyvarinen et al.), one must install the fastica toolbox and include it in the Matlab path. Details of how these ICA algorithms work can be found in the scientific papers of the teams that developed them. In general, the physiological significance of any differences in the results or different algorithms (or of different parameter choices in the various algorithms) have not been tested -- neither by us nor, as far as we know, by anyone else. Applied to simulated, relatively low dimensional data sets for which all the assumptions of ICA are exactly fulfilled, all three algorithms return near-equivalent components. We are satisfied that Infomax ICA (runica/binica) gives stable decompositions with up to hundreds of channels (assuming enough training data are given, see below), and therefore we can recommend its use, particularly in its faster binary form (binica.m - note that you will also need to download the binary file at this page and edit the EEGLAB file icadefs.m so that it points to the right binary). Note about jader: this algorithm uses 4th-order moments (whereas Infomax uses (implicitly) a combination of higher-order moments) but the storage required for all the 4th-order moments become impractical for datasets with more than ~50 channels. Note about fastica: Using default parameters, this algorithm quickly computes individual components (one by one). However, the order of the components it finds cannot be known in advance, and performing a complete decomposition is not necessarily faster than Infomax. Thus for practical purposes its name for it should not be taken literally. Also, in our experience it may be less stable than Infomax for high-dimensional data sets.
Very important note: We usually run ICA using many more trials that the sample decomposition presented here. As a general rule, finding Nstable components (from N-channel data) typically requires more than kN^2 data sample points (at each channel), where N^2 is the number of weights in the unmixing matrix that ICA is trying to learn and k is a multiplier. In our experience, the value of k increases as the number of channels increases. In our example using 32 channels, we have 30800 data points, giving 30800/32^2 = 30 pts/weight points. However, to find 256 components, it appears that even 30 points per weight is not enough data. In general, it is important to give ICA as much data as possible for successful training. Can you use too much data? This would only occur when data from radically different EEG states, from different electrode placements, or containing non-stereotypic noise were concatenated, increasing the number of scalp maps associated with independent time courses and forcing ICA to mixture together dissimilar activations into the N output components. The bottom line is: ICA works best when given a large amount of basically similar and mostly clean data. When the number of channels (N) is large (>>32) then a very large amount of data may be required to find N components. When insufficient data are available, then using the 'pca' option to jader.m to find fewer than N components may be the only good option.
Supported Systems for binica: To use the optional (and much faster) binica, which calls binica.m , the faster C translation of runica.m, you must make the location of the executable ICA file known to Matlab and executable on your system. Note: Edit the EEGLAB icadefs.m Matlab script file to specify the location of the binica.m executable. The EEGLAB toolbox includes three versions of the binary executable Informax ica routine, for Linux (compiled under Redhat 2.4), freebsd (3.0) and freebsd (4.0) (these are named, respectively ica_linux2.4 , ica_bsd3.0 and ica_bsd4.0). The executable file must also be accessible through the Unix user path variable otherwise binica.m won't work. Windows and sun version (older version of binary ICA executable) are available here (copy them to the EEGLAB directory). Please contact us to obtain the latest source code to compile it on your own system.
Channel types: It is possible to select specific channel types (or even a list of channel numbers) to use for ICA decomposition. For instance, if you have both EEG and EMG channels, you may want to run ICA on EEG channels only, since any relationship between EEG and EMG signals should involve propagation delays and ICA assumes an instantaneous relationship (e.g., common volume conduction). Use the channel editor to define channel types.
Running runica produces the following text on the Matlab command line:
Input data size [32,30720] = 32 channels, 30720 frames. Finding 32 ICA components using logistic ICA. Initial learning rate will be 0.001, block size 36. Learning rate will be multiplied by 0.9 whenever angledelta >= 60 deg. Training will end when wchange < 1e-06 or after 512 steps. Online bias adjustment will be used. Removing mean of each channel ... Final training data range: -145.3 to 309.344 Computing the sphering matrix... Starting weights are the identity matrix ... Sphering the data ... Beginning ICA training ... step 1 - lrate 0.001000, wchange 1.105647 step 2 - lrate 0.001000, wchange 0.670896 step 3 - lrate 0.001000, wchange 0.385967, angledelta 66.5 deg step 4 - lrate 0.000900, wchange 0.352572, angledelta 92.0 deg step 5 - lrate 0.000810, wchange 0.253948, angledelta 95.8 deg step 6 - lrate 0.000729, wchange 0.239778, angledelta 96.8 deg ... step 55 - lrate 0.000005, wchange 0.000001, angledelta 65.4 deg step 56 - lrate 0.000004, wchange 0.000001, angledelta 63.1 deg Inverting negative activations: 1 -2 -3 4 -5 6 7 8 9 10 -11 -12 -13 -14 -15 -16 17 -18 -19 -20 -21 -22 -23 24 -25 -26 -27 -28 -29 -30 31 -32 Sorting components in descending order of mean projected variance ... 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Note: the runica Infomax algorithm can only select for components with a supergaussian activity distribution (i.e., more highly peaked than a Gaussian, something like an inverted T). If there is strong line noise in the data, it is preferable to enter the option 'extended', 1 in the command line option box, so the algorithm can also detect subgaussian sources of activity, such as line current and/or slow activity.
Another option we often use is the stop option: try 'stop', 1E-7 to lower the criterion for stopping learning, thereby lengthening ICA training but possibly returning cleaner decompositions, particularly of high-density array data. We also recommend the use of collections of short epochs that have been carefully pruned of noisy epochs (see Rejecting artifacts with EEGLAB).
In the commandline printout, the angledelta is the angle between the direction of the vector in weight space describing the current learning step and the direction describing the previous step. An intuitive view of the annealing angle (angledelta) threshold (see above) is as follows: If the learning step takes the weight vector (in global weight vector space) 'past' the true or best solution point, the following step will have to 'back-track.' Therefore, the learning rate is too high (the learning steps are too big) and should be reduced. If, on the other hand, the learning rate were too low, the angle would be near 0 degrees, learning would proceed in (small) steps in the same direction, and learning would be slow. The default annealing threshold of 60 degrees was arrived at heuristically, and might not be optimum.
Note: the runica Infomax function returns two matrices, a data sphering matrix (which is used as a linear preprocessing to ICA) and the ICA weight matrix. For more information, refer to ICA help pages (i.e. http://www.sccn.ucsd.edu/~arno/indexica.html). If you wish, the resulting decomposition (i.e., ICA weights and sphere matrices) can then be applied to longer epochs drawn from the same data, e.g. for time-frequency decompositions for which epochs of 3-sec or more may be desirable.
The component order returned by runica/binica is in decreasing order of the EEG variance accounted for by each component. In other words, the lower the order of a component, the more data (neural and/or artifactual) it accounts for. In contrast to PCA, for which the first component may account for 50% of the data, the second 25%, etc..., ICA component contributions are much more homogeneous, ranging from roughly 5% down to ~0%. This is because PCA specifically makes each successive component account for as much as possible of the remaining activity not accounted for by previously determined components -- while ICA seeks maximally independent sources of activity.
PCA components are temporally or spatially orthogonal - smaller component projections to scalp EEG data typically looking like checker boards - while ICA components of EEG data are maximally temporally independent, but spatially unconstrained -- and therefore able to find maps representing the projection of a partially synchronized domain / island / patch / region of cortex, no matter how much it may overlap the projections of other (relatively independent) EEG sources. This is useful since, apart from ideally (radially) oriented dipoles on the cortical surface (i.e., on cortical gyri, not in sulci), simple biophysics shows that the volume projection of each cortical domain must project appreciably to much of the scalp.
Important note: Run twice on the same data, ICA decompositions under runica/binica will differ slightly. That is, the ordering, scalp topography and activity time courses of best-matching components may appear slightly different. This is because ICA decomposition starts with a random weight matrix (and randomly shuffles the data order in each training step), so the convergence is slightly different every time. Is this a problem? At the least, features of the decomposition that do not remain stable across decompositions of the same data should not be interpreted except as irresolvable ICA uncertainty.
Differences between decompositions trained on somewhat different data subsets may have several causes. We have not yet performed such repeated decompositions and assessed their common features - though this would seem a sound approach. Instead, in our recent work we have looked for commonalities between components resulting from decompositions from different subjects.
Plotting 2-D Component Scalp Maps
To plot 2-D scalp component maps, select Plot > Component maps > In 2-D. The interactive window (below) is then produced by function pop_topoplot.m . It is similar to the window we used for plotting ERP scalp maps. Simply press OK to plot all components.
Note: This may take several figures, depending on number of channels and the Plot geometry field parameter. An alternative is to call this functions several times for smaller groups of channels (e.g., 1:30 , 31:60 , etc.). Below we ask for the first 12 components (1:12) only, and choosing to set 'electrodes', 'off'.
The following topoplot.m window appears, showing the scalp map projection of the selected components. Note that the scale in the following plot uses arbitrary units. The scale of the component's activity time course also uses arbitrary units. However, the component's scalpmap values multiplied by the component activity time course is in the same unit as the data. [Note, 09/18: The ICLabel plug-in of Luca Pion-Tonachini now provides the best estimation of the type of each of the independent components (brain, eye, muscle, line noise, etc.). The included function viewprops() makes a display like that below, but with component type labels -- and clicking on a component will pop up a window with an expanded set of component property measures, as well as the estimated probabilities of each component being of each type. -SM]
Learning to recognize types of independent components may require experience. The main criteria to determine if a component is 1) cognitively related 2) a muscle artifact or 3) some other type of artifact are, first, the scalp map (as shown above), next the component time course, next the component activity power spectrum and, finally (given a dataset of event-related data epochs), the erpimage.m. For example an expert eye would spot component 3 (above) as an eye artifact component (see also component activity by calling menu Plot > Component activations (scroll)). In the window above, click on scalp map number 3 to pop up a window showing it alone (as mentioned earlier, your decomposition and component ordering might be slightly different).
Note: From EEGLAB v4.32 on, if the electrode montage extends below the horizontal plane of the head center, topoplot.m plots them as a 'skirt' (or halo) around the cartoon head that marks the (arc_length = 0.5) head-center plane. (Note: Usually, the best-fitting sphere is a cm or more above the plane of the nasion and ear canals). By default, all channels with location arc_lengths <= 1.0 (head bottom) are used for interpolation and are shown in the plot. From the commandline, topoplot.m allows the user to specify the interpolation and plotting radii (intrad and plotrad) as well as the radius of the cartoon head (headrad). The headrad value should normally be kept at its physiologically correct value (0.5). In 'skirt' mode (see below), the cartoon head is plotted at its normal location relative to the electrodes, the lower electrodes appearing in a 'skirt' outside the head cartoon. If you have computed an equivalent dipole model for the component map (using the DIPFIT plug-in) topoplot.m can indicate the location of the component equivalent current dipole(s). Note that the 'balls' of the dipole(s) are located as if looking down from above on the transparent head. The distance of the electrode positions from the vertex, however, is proportional to their (great circle) distance on the scalp to the vertex. This keeps the electrodes on the sides of the head from being bunched together as they would be in a top-down view of their positions. This great-circle projection spreads out the positions of the lower electrodes. Thus, in the figure below, the (bottom) electrodes plotted on the lower portion of the 'skirt' are actually located on the back of the neck. In the plot, they appear spread out, whereas in reality they are bunched on the relatively narrow neck surface. The combinations of top-down and great-circle projections allows the full component projection (or raw data scalp map) to be seen clearly, while allowing the viewer to estimate the actual 3-D locations of plot features.
The EEGLAB v4.32 topoplot.m above shows an independent component whose bilateral equivalent dipole model had only 2% residual variance across all 252 electrode locations. This binica.m decomposition used PCA to reduce the over 700,000 data points to 160 principal dimensions (a ratio of 28 time points per ICA weight).
Plotting component headplots
Using EEGLAB, you may also plot a 3-D head plot of a component topography by selecting Plot > Component maps > In 3-D. This calls pop_headplot.m. The function should automatically use the spline file you have generated when plotting ERP 3-D scalp maps. Select one ore more components (below) and press OK. For more information on this interface and how to perform coregistration, see the Plotting ERP Data in 3-D and the DIPFIT.
The pop_headplot.m window below appears. You may use the Matlab rotate 3-D option to rotate these headplots with the mouse. Else, enter a different view angle in the window above.
Studying and removing ICA components
To study component properties and label components for rejection (i.e. to identify components to subtract from the data), select Tools > Reject data using ICA > Reject components by map. The difference between the resulting figure(s) and the previous 2-D scalp map plots is that one can here plot the properties of each component by clicking on the rectangular button above each component scalp map.
For example, click on the button labeled 3. This component can be identified as an eye artifact for three reasons:
- The smoothly decreasing EEG spectrum (bottom panel) is typical of an eye artifact;
- The scalp map shows a strong far-frontal projection typical of eye artifacts; And,
- It is possible to see individual eye movements in the component erpimage.m (top-right panel).
Eye artifacts are (nearly) always present in EEG datasets. They are usually in leading positions in the component array (because they tend to be big) and their scalp topographies (if accounting for lateral eye movements) look like component 3 or perhaps (if accounting for eye blinks) like that of component 10 (above). Component property figures can also be accessed directly by selecting Plot > Component properties. (There is an equivalent menu item for channels, Plot > Channel properties). Artifactual components are also relatively easy to identify by visual inspection of component time course (menu Plot > Component activations (scroll) --- not shown here).
Since this component accounts for eye activity, we may wish to subtract it from the data before further analysis and plotting. If so, click on the bottom green Accept button (above) to toggle it into a red Reject button (note: at this point, components are only marked for rejection; to subtract marked components, see next section 'Subtracting ICA components from data'). Now press OK to go back to the main component property window.
Another artifact example in our decomposition is component 32, which appears to be typical muscle artifact component. This components is spatially localized and show high power at high frequencies (20-50 Hz and above) as shown below.
Artifactual components often encountered (but not present in this decomposition) are single-channel (channel-pop) artifacts in which a single channel goes 'off,' or line-noise artifacts such as 23 (the ERP image plot below shows that it picked up some noise line at 60 Hz especially in trials 65 and on).
Many other components appear to be brain-related (Note: Our sample decomposition used in this tutorial is based on clean EEG data, and may have fewer artifactual components than decompositions of some other datasets). The main criteria for recognizing brain-related components are that they have:
- Dipole-like scalp maps,
- Spectral peaks at typical EEG frequence is (i.e., 'EEG-like' spectra) and,
- Regular ERP-image plots (meaning that the component does not account for activity occurring in only a few trials).
The component below has a strong alpha band peak near 10 Hz and a scalp map distribution compatible with a left occipital cortex brain source. When we localize ICA sources using single-dipole or dipole-pair source localization. Many of the 'EEG-like' components can be fit with very low residual variance (e.g., under 5%). See the tutorial example for either the EEGLAB plug-in DIPFIT or for the BESA plug-in for details.
What if a component looks to be "half artifact, half brain-related"? In this case, we may ignore it, or may try running ICA decomposition again on a cleaner data subset or using other ICA training parameters. As a rule of thumb, we have learned that removing artifactual epochs containing one-of-a-kind artifacts is very useful for obtaining 'clean' ICA components.
Important note: we believe an optimal strategy is to:
- Run ICA
- Reject bad epochs (see the functions we developed to detect artifactual epochs and channels, if any, in the tutorial on artifact rejection). In some cases, we do not hesitate to remove more than 10% of the trials, even from 'relatively clean' EEG datasets. We have learned that it is often better to run this first ICA composition on very short time windows.
- Run ICA a second time on the 'pruned' dataset.
- Apply the resulting ICA weights to the same dataset or to longer epochs drawn from the same original (continuous or epoched) dataset. For instance, to copy ICA weights and sphere information from dataset 1 to 2: First, call menu Edit > Dataset info of dataset 2. Then enter ALLEEG(1).icaweights in the ICA weight array ... edit box, ALLEEG(1).icasphere in the ICA sphere array ... edit box, and press OK.
How to deal with "corrupted" ICA decompositions
When using Infomax ICA, which is the default in EEGLAB, it may happen that the first two components' activity blows up. This happens because the two components' activity compensate for each other. In this case, both components are seen as having a large amount of noise. This is illustrated below.
The solution to this is not obvious. One solution is to use a different ICA algorithm. Another solution we have been using is to experiment with decreasing the number of dimension using PCA. For example, in one case with 32 channels, decreasing the number of dimension to 10 eliminates the problem (decreasing to 20 did not). Below is the same data but decomposed with only 10 PCA components. The first two components clearly isolates the blinks as they did before but do not appear as noisy. We are not certain that removing the single blink component below is preferable to removing the two very noisy component above since we have not run any formal comparison. Our reasoning is that the two component above tend to make other components noisy as well so the solution where dimensions are reduced by PCA is preferable.
This is not to say that using PCA should be done systematically. In general, PCA will slightly corrupt the data by adding non linearities so it is better to use the full rank data matrix whenever possible.
Subtracting ICA components from data
Typically we (at SCCN) don't actually subtract whole independent component processes from our datasets because typically we study individual component (rather than summed scalp channel) activities. However, if and when we want to remove components, we use menu Tools > Remove components, which calls the pop_subcomp.m function. The component numbers present by default in the resulting window (below) are those marked for rejection in the previous Tools > Reject using ICA > Reject components by map component rejection window (using the Accept/Reject buttons). Enter the component numbers you wish to reject and press OK.
A window will pop up, plotting channel ERP before (in blue) and after (in red) component(s) subtraction and asking you if you accept the new ERP.
Press Yes. A last window will pop up asking you if you want to rename the new data set. Give it a name and again press OK.
Note that storing the new dataset in Matlab memory does not automatically store it permanently on disk. To do this, select File > Save current dataset. Note that we will pursue with all components, so you can appreciate the contribution of artifactual components to the ERP. You may recover the previous dataset using the Dataset top menu.
Note: If you try to run ICA on this new dataset, the number of dimensions of the data will have been reduced by the number of components subtracted. To run ICA on the reduced dataset, use the pca option under the Tools > Run ICA pop-up window, type 'pca', '10' in the Commandline options box to reduce the data dimensions to the number of remaining components (here 10), before running ICA (see runica.m. If the amount of data has not changed, ICA will typically return the same (remaining) independent components -- which were, after all, already found to be maximally independent for these data. After running ICA (not before), we suggest you again 'baseline-zero' the data; if it is epoched when some components have been removed, channel data epoch-baseline means may differ.
Retaining multiple ICA weights in a dataset
To retain multiple copies of ICA weights (e.g. EEG.weights and EEG.sphere), use the extendibility property of Matlab structures. On the Matlab command line, simply define new weight and sphere variables to retain previous decomposition weights. For example,
>> EEG.icaweights2 = EEG.icaweights; % Store existing ICA weights matrix >> EEG.icasphere2 = EEG.icasphere; % Store existing ICA sphere matrix >> [ALLEEG EEG] = eeg_store(ALLEEG, EEG, CURRENTSET); % copy to EEGLAB memory >> EEG = pop_runica(EEG); % Compute ICA weights and sphere again using % binica() or runica(). Overwrites new weights/sphere matrices % into EEG.icaweights, EEG.icasphere >> [ALLEEG EEG] = eeg_store(ALLEEG, EEG, CURRENTSET); % copy to EEGLAB memory
Both sets of weights will then be saved when the dataset is saved, and reloaded when it is reloaded. See the script tutorial for more information about writing Matlab scripts for EEGLAB.
Scrolling through component activations
To scroll through component activations (time courses), select Plot > Component activations (scroll). Scrolling through the ICA activations, one may easily spot components accounting for characteristic artifacts. For example, in the scrolling eegplot.m below, component 3 appears to account primarily for blinks. Check these classifications using the complementary visualization produced by Plot > Component properties.
In the next tutorial, we show more ways to use EEGLAB to study ICA components of the data.
Issue: ICA returns near-identical components with opposite polarities
When computing average reference on n-channel data, the rank of the data is reduced to n-1. Why? Because the sum of the potential is 0 at all time points, the last channel activity is equal to minus the sum of the others. ICA does not behave well in this (rank-deficient) condition. Below, we show an ICA solution computed on data which was average referenced and in which two of the returned components are almost identical with opposite polarities (data collected with EGI amplifier, courtesy of the Institute of Noetic Sciences).
There are 30 channels shown above; the time width is 5 sec. When the same runica() or binica() (or from the gui, pop_runica()) decomposition is run using the option "'pca', 29", then a single similar (but not quite identical) component is returned, and here exhibits quite well defined alpha activity. Not only does this component account for both component activities above, but the noise in their activitations disappears (i.e., is more properly assigned by ICA to other component processes). The noise above is most likely due to instability in the ICA decomposition algorithm, which is here forced to create two components compensating for each others activity.
If the rank of the data is lower than the number of channels, the EEGLAB pop_runica() function should detect it. However, rank calculation in Matlab is imprecise, especially since raw EEG data is stored at single precision. There are thus some cases in which the rank reduction arising from use of average reference is not detected. In this case, the user should reduce manually the number of components decomposed. For example, when using 64 channels enter, in the option edit box, "'pca', 63". If you do not do this, the activity of one of the components that contributes the most to the data might be duplicated (as shown above) and you will not be usable for your analysis. The activity of other components does not seem much affected in our experience, though as the figure above shows the component affected may also take on noise.