[Eeglablist] ICA components distorted after rereferencing, saving and reloading

Mon Aug 4 10:34:44 PDT 2025

Tomko,

> you’re saying that when I account for the rank deficiency using this method, I don’t necessarily need to add an all-zero reference channel — correct?

No, again, I'm saying they are 2 different thing:
- The modified CAR is to avoid a rank reduction when you re-reference to average (you should always use the modified version to avoid losing a rank).
- The rank estimation and PCA reduction is to ensure ICA is never vulnerable to the rank deficiency issue (whatever the cause is). 

Just use both by default from now on, so you 1) never loose 1 rank when re-referencing to average, and 2) never accidentally run ICA on rank deffficient data. That's it.

Here is the code again for the modified CAR, independent of what your initial reference was:

% Calculate effective data rank before CAR
dataRank = sum(eig(cov(double(EEG.data')))>1E-7)  % for continuous data

% Define label for the surrogate channel
refLabel = 'initialReference';

% Create dummy location struct from an existing channel to preserve field structure
tmpLoc = EEG.chanlocs(1);
tmpLoc.labels = refLabel;
tmpLoc.X = []; tmpLoc.Y = []; tmpLoc.Z = [];
tmpLoc.theta = []; tmpLoc.radius = []; tmpLoc.type = '';

% Apply average re-reference including the dummy channel (pop_reref() 
% automatically appends a zero-filled channel when 'refloc' is provided)
EEG = pop_reref(EEG, [], 'refloc', tmpLoc);

% Remove the dummy channel to restore original channel count
EEG = pop_select(EEG, 'nochannel', {refLabel});

% Calculate effective data rank after CAR (should be same as before)
dataRank = sum(eig(cov(double(EEG.data')))>1E-7)  % for continuous data

And your code comment says you are removing bad channels, but you are also running ASR in that command. To be very clear, you can separate the two like this for example:

% Remove bad channels (data must be highpass filtered!)
EEG = pop_clean_rawdata(EEG, 'FlatlineCriterion',5,'ChannelCriterion',0.75,'LineNoiseCriterion',10,'Highpass','off','BurstCriterion','off','WindowCriterion','off','BurstRejection','off','Distance','Euclidian');

% Remove very large artifacts with very conservative threshold to improve ICA decomposition (preserving eye blinks)
EEG = pop_clean_rawdata(EEG, 'FlatlineCriterion','off', 'ChannelCriterion','off','LineNoiseCriterion','off','Highpass','off', 'BurstCriterion',100,'WindowCriterion','off','BurstRejection','on', 'Distance','Euclidian','WindowCriterionTolerances','off' );

If you see too many large artifacts remain, lower threshold to 60-80 range). If you see eye blinks being removed and you don't have large artifacts in the data in the first place, don't do this step. 

Cedric

On Monday, August 4th, 2025 at 7:02 AM, Tomko Settgast <tomko.settgast at uni-wuerzburg.de> wrote:

> Hi Cédric,
>
> First of all, thanks again for your helpful and quick reply!
> I really appreciate your thoughtful explanations and your patience with my questions.
> Also, apologies for the late response — I’m currently on vacation and only checking emails sporadically.
>
> Regarding your last message: I think I may have expressed myself a bit ambiguously. I did not mean to suggest adjusting the threshold itself (i.e., the 1e-7), but rather that I use your method with that threshold to estimate the effective rank and adapt the ICA accordingly.
>
> If I understood you correctly, you’re saying that when I account for the rank deficiency using this method, I don’t necessarily need to add an all-zero reference channel — correct?
> Of course, it would be ideal to have a full-rank dataset, but realistically my data is often so noisy that I usually have to delete and interpolate several channels. So I'm not aiming for full rank in any case.
>
> Would you still recommend introducing the zero-filled reference channel, perhaps especially in my case due to the substantial data loss from interpolation?
>
> Just for context: the EEG data was recorded with an online earlobe reference, and unfortunately, the original reference signal is not available in the recordings.
>
> From our discussion so far, here’s what I would consider the most appropriate pipeline:
>
> 1. Identify and remove bad channels using clean_rawdata
> 2. Interpolate deleted channels
> 3. Apply CAR
> 4. Estimate rank using eigenvalue threshold
> 5. Run ICA with PCA reduction (according to the effective rank) if necessary
> 6. Apply MARA to remove artifact components
>
> If you're interested, I’ve copied the relevant part of my code below. I'd be very happy about any feedback:
>
> % [Initial steps: save structure, channel labels, etc.]
>
> % Run clean_rawdata to remove bad channels
> EEG = clean_artifacts(EEG, 'FlatlineCriterion',5,'ChannelCriterion',0.85,...
> 'LineNoiseCriterion',4,'Highpass','off','BurstCriterion',5,...
> 'WindowCriterion',0.25,'BurstRejection','off','Distance','Euclidian',...
> 'WindowCriterionTolerances',[-Inf 7] );
> EEG = eeg_checkset( EEG );
>
> % Interpolate deleted channels
> EEG = pop_interp(EEG, originalEEG.chanlocs, 'spherical');
> EEG = eeg_checkset( EEG );
>
> % Log interpolated channels to EEG.etc.ica_info
> % [...]
>
> % Re-reference to common average
> EEG = pop_reref( EEG, []);
> EEG = eeg_checkset(EEG);
>
> % Estimate rank using eigenvalue threshold
> data2d = reshape(EEG.data, EEG.nbchan, []);
> covarianceMatrix = cov(double(data2d'));
> eigenvalues = eig(covarianceMatrix);
> rankThreshold = 1e-7;
> dataRank = sum(eigenvalues > rankThreshold);
>
>
> % Save rank-related info
> % [...]
>
> % Run ICA with PCA if needed
> if dataRank < EEG.nbchan
> EEG = pop_runica(EEG, 'icatype', 'runica', 'extended', 1, 'pca', dataRank, 'interrupt', 'on');
> else
> EEG = pop_runica(EEG, 'icatype', 'runica', 'extended', 1, 'interrupt', 'on');
> end
> EEG = eeg_checkset(EEG);
>
> % Run MARA and reject components
> [artcomps, MARAinfo] = MARA(EEG);
> EEG.reject.MARAinfo = MARAinfo;
> EEG.reject.gcompreject(artcomps) = 1;
> EEG = pop_subcomp(EEG, find(EEG.reject.gcompreject));
> EEG = eeg_checkset(EEG);
> Do you think that would work?
>
> Do you think this approach would be robust enough given the limitations of my data?
>
> If anything is unclear or if you'd like me to elaborate on any step, just let me know!
>
> Best regards from my vacation,
> Tomko 😊
>
> -----Ursprüngliche Nachricht-----
> Von: eeglablist eeglablist-bounces at sccn.ucsd.edu Im Auftrag von
>
> Cedric
>
>
> Cannard via eeglablist
>
> Gesendet: Freitag, 1. August 2025 09:42
> An: EEGLAB List eeglablist at sccn.ucsd.edu
>
> Betreff: [EXT] Re: [Eeglablist] ICA components distorted after rereferencing, saving and reloading
>
> Hi Tomko,
>
> Ah—if you’re referring to the 1e-7, no, you should never need to change that. That threshold is used to evaluate the data rank anccurately and ensure any deficiencies are properly accounted for when passed to runica(). The modified common average reference (CAR) method is designed to avoid losing a rank in the first place, but even if there were a reduction, providing the correct data rank to ICA handles it. Ideally, of course, you want to preserve as much information from your original data as possible, and the modified CAR helps maintain full rank during average referencing.
>
> More severe rank reductions can occur when, for example, multiple bad channels are removed and interpolated—say, 5 out of 64, leaving you with an effective rank of 59. Additional rank loss can result from other, less visible factors like bridged electrodes or crosstalk due to poor shielding.
>
> In short, the modified referencing method helps preserve rank, and the command-line ICA implementation needs the actual data rank passed in to function properly.
>
> Note that this is handled automatically in the EEGLAB GUI, but it’s important to be aware of when scripting ICA manually.
>
> Makes sense?
>
> Cédric
>
> On Thu, Jul 31, 2025 at 07:07, Tomko Settgast <[tomko.settgast at uni-wuerzburg.de](mailto:On Thu, Jul 31, 2025 at 07:07, Tomko Settgast <<a href=)> wrote:
>
> > Dear Cedric,
> >
> > I am sorry for having caused some confusion!
> > What I meant with "threshold" was the right part of the function you have referred to in your paper: sum(eig(cov(double(EEG.data'))) > 1e-7).
> >
> > So, my question was: if I use this method to estimate and account for rank deficiencies when doing ICA, do I still need to introduce the zero-filled reference channel first, or does this approach already account for the rank-deficiencies introduced by not having a reference channel and applying CAR before ICA?
> >
> > Regarding clean_rawdata: you mentioned removing channels using a lax threshold before running the cleaning step. I assumed this meant running clean_rawdata twice — once with a lax threshold to remove obviously bad channels, and then again with stricter/default settings. Is that correct? And if so, do you apply both passes of clean_rawdata in direct sequence, or do you insert ICA in between (i.e., clean_rawdata (lax), ICA, clean_rawdata ("normal"))?
> > Or do you use another method to detect very noisy channels prior to cleaning?
> >
> > I hope this clarifies my questions a bit better.
> >
> > Best,
> > Tomko
> >
> > -----Original Message-----
> > From: eeglablist eeglablist-bounces at sccn.ucsd.edu On Behalf Of
> > Cedric Cannard via eeglablist
> > Sent: Thursday, July 31, 2025 8:28 AM
> > To: EEGLAB List eeglablist at sccn.ucsd.edu
> > Subject: [EXT] Re: [Eeglablist] ICA components distorted after
> > rereferencing, saving and reloading
> >
> > Hi Tomko,
> >
> > > using your threshold and applying PCA reduction during ICA alone cannot account for existing rank deficiencies, correct?
> > > So in any case, I would still need to re-introduce this zero-filled reference channel?
> >
> > "threshold" referring to ASR/clean_rawdata() here? That is just to remove large artifacts before ICA.
> >
> > Feeding the effective data rank to ICA ensures that it accounts for rank deficiencies in the data. For instance, if your dataset has 64 electrodes but only 60 effective dimensions (e.g., due to applying a common average reference, which reduces rank by 1, and interpolating 3 bad channels), then ICA will only attempt to extract 60 independent components instead of 64, which prevents overfitting and instability.
> >
> > Using the modified CAR approach (where a simulated channel filled with zeros is appended before re-referencing) allows you to maintain full rank during average referencing. This way, you preserve the original data rank and don’t need to reintroduce the reference channel later.
> >
> > Hope this helps,
> >
> > Cedric Cannard
> >
> > On Wednesday, July 30th, 2025 at 4:44 AM, Tomko Settgast tomko.settgast at uni-wuerzburg.de wrote:
> >
> > > Dear
> > >
> > > Cedric
> > >
> > > ,
> > >
> > > Thanks again for the super quick and helpful reply!
> > > And yes, of course, it makes sense to bring this back to the community.
> > >
> > > I will try to follow your recommendations.
> > > Just to be sure: using your threshold and applying PCA reduction during ICA alone cannot account for existing rank deficiencies, correct?
> > > So in any case, I would still need to re-introduce this zero-filled reference channel?
> > >
> > > Regarding your second recommendation - to clarify: Do you apply the clean_rawdata twice directly in sequence (first with a lax threshold, then with a stricter one), or do you run ICA in between, i.e., clean_rawdata (lax), ICA, clean_rawdata ("normal")?
> > >
> > > Thanks again!
> > >
> > > Best,
> > > Tomko 😊
> > >
> > > -----Ursprüngliche Nachricht-----
> > > Von: eeglablist eeglablist-bounces at sccn.ucsd.edu Im Auftrag von
> > > Cedric Cannard via eeglablist
> > >
> > > Gesendet: Dienstag, 29. Juli 2025 23:01
> > > An: EEGLAB List eeglablist at sccn.ucsd.edu
> > >
> > > Betreff: [EXT] Re: [Eeglablist] ICA components distorted after
> > > rereferencing, saving and reloading
> > >
> > > Dear Tomko,
> > >
> > > Getting this thread back in the eeglablist loop in case it is helpful to more people later.
> > >
> > > Yes, apply the modified CAR method (zero-filled channel) to preserve effective data rank before ICA:
> > >
> > > % 1) Add a zero-filled surrogate for the initial reference refLabel =
> > > 'initialReference'; EEG.data(end+1, :) = 0; EEG.nbchan = EEG.nbchan +
> > > 1; EEG.chanlocs(end+1).labels = refLabel; % minimal fields are fine
> > > for pop_reref
> > >
> > > % 2) Re-reference to average including the zero-filled reference % use 'refloc' input to explicitly tell EEGLAB that you are referencing to a virtual reference with the label 'initialReference'. EEGLAB stores this information in EEG.ref and internally handles bookkeeping better for projection matrices, reversing the reference or re-referencing later.
> > > EEG = pop_reref(EEG, [], 'refloc', struct('labels', refLabel));
> > >
> > > % 3) Remove the zero-filled channel to return to the original channel
> > > count EEG = pop_select(EEG, 'nochannel', {refLabel});
> > >
> > > Note: when I can, I generally run clean_rawdata/ASR right after removing bad channels with a very lax threshold (e.g. 60-100 depending on data) to remove large artifacts before ICA to improve performance. More aggressive thresholds can remove eye blinks or alpha waves depending on your data quality, which would then reduce ICA performance at extracting ocular components (you want to preserve the eye activity to separate it more successfully).
> > >
> > > Cedric
> > >
> > > On Saturday, July 26th, 2025 at 4:07 AM, Tomko Settgast tomko.settgast at uni-wuerzburg.de wrote:
> > >
> > > > Dear Dr. Cannard,
> > > >
> > > > Thank you very much for the quick and helpful response!
> > > >
> > > > As far as I recall your paper, I also do not remember you recommending re-referencing after ICA.
> > > > I was trying to follow both the guidelines you provided in your paper, and the recommendation from the EEGLab tutorial.
> > > > That is what me lead to this hybrid approach.
> > > > But knowing that the recommendation in EEGLab might be incorrect actually simplifies things for me because I did not observe any problems when applying CAR before ICA.
> > > > What may have added to my confusion - making the recommendation in the tutorial plausible - was that some pipelines may indeed appear to apply CAR after ICA (e.g., EPOS: https://urldefense.com/v3/__https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.660449/full*h4__;Iw!!Mih3wA!GU-3iC3bMIbuyzXxfHhwMjWvX_aw0Qc2OYibKegEIojxdaSycLRawNNpcStKWPGQT6A1y0gonLnCTlzxbuFdoHs42g$ , or HAPPE: https://urldefense.com/v3/__https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00097/full*h5__;Iw!!Mih3wA!GU-3iC3bMIbuyzXxfHhwMjWvX_aw0Qc2OYibKegEIojxdaSycLRawNNpcStKWPGQT6A1y0gonLnCTlzxbuGqsNTIrA$ ).
> > > > However, I don't know their implementations in detail, and they might have already accounted for occurring issues.
> > > >
> > > > I especially want to thank you for the detailed explanation regarding why saving and reloading seemingly corrupts the ICA.
> > > > That was the thing that puzzled me the most and they way you have explained it made immediate sense.
> > > >
> > > > Just to confirm: If I understood you correctly, you would recommend the following order: Delete heavily noisy or flat channels (I am using clean_artifcacts), interpolate these channels, apply CAR, run ICA, correct?
> > > >
> > > > One additional question regarding the rank-deficiency issues in ICA:
> > > > My datasets do not include a reference channel and were recorded with a unipolar reference (ear lobe/CZ).
> > > > In Makoto's pipeline, if I understood it correctly, it is suggested to add zero-filled channel to account for the effective rank-deficiency when no reference channel is present.
> > > > Now, if I follow your recommendations, i.e., computing the approximate true rank with the formula you provided - do you think this is already sufficient, or would you still recommend adding a zero-only channel to my data?
> > > >
> > > > Thank you again for you time and the guidance!
> > > >
> > > > Best,
> > > > Tomko
> > > >
> > > > -----Ursprüngliche Nachricht-----
> > > > Von: eeglablist eeglablist-bounces at sccn.ucsd.edu Im Auftrag von
> > > >
> > > > Cedric
> > > >
> > > > Cannard via eeglablist
> > > >
> > > > Gesendet: Freitag, 25. Juli 2025 19:59
> > > > An: EEGLAB List eeglablist at sccn.ucsd.edu
> > > >
> > > > Betreff: [EXT] Re: [Eeglablist] ICA components distorted after
> > > > rereferencing, saving and reloading
> > > >
> > > > Hi Tomko,
> > > >
> > > > There may be an error in the tutorial web page. Makoto, Scott, or Arno, please correct me if I'm wrong here.
> > > >
> > > > If I recall correctly, we do not recommend in the paper to re-reference after ICA. Instead, we emphasize correcting the average referencing before ICA and taking the data rank into account while computing ICA, so that ICA operates on a properly rank-full dataset and avoids generating "ghost ICs".
> > > >
> > > > see these quotes:
> > > > "To avoid this issue, we propose two solutions: 1) apply the correct average referencing, and 2) calculate the effective data rank that is used for PCA dimension reduction in applying ICA."
> > > > "The correct method, i.e., including the initial reference when re-referencing and then discarding the initial reference channel, resulted in a successful rank-full decomposition."
> > > >
> > > > Now to address your questions more directly:
> > > >
> > > > EEGLAB stores the ICA decomposition using:
> > > > - EEG.icaweights and EEG.icasphere: together they define the
> > > > unmixing matrix,
> > > > - EEG.icawinv: the mixing matrix (inverse of the unmixing matrix),
> > > > - EEG.data: the referenced data ICA was trained on.
> > > > When you apply pop_reref(EEG, []) after ICA, it modifies EEG.data, but does not update the ICA weights or sphere, nor does it recompute EEG.icaact if it exists. So now the ICA decomposition no longer corresponds to the modified data (that is if operations were done between your last referencing and post-ICA referencing). If you save the set like this, ICA reconstruction is no longer valid when reloading.
> > > > --> This is why the ICs appear corrupted after reloading — the weights are applied to differently referenced data than they were trained on.
> > > >
> > > > Why does the issue only happen when MARA doesn’t reject any components? I haven't used MARA before, but my guess is that when MARA rejects components, EEGLAB calls pop_subcomp() which updates EEG.data to reflect the projection of the ICA decomposition with removed components. This may result in reinitializing the ICA fields in a way that masks the corruption caused by re-referencing.
> > > >
> > > > But when no components are rejected (gcompreject = 0), EEG.data is left unchanged from ICA, and then your subsequent call to pop_reref corrupts the ICA-to-data correspondence.
> > > >
> > > > > Can you confirm whether re-referencing after MARA might break the ICA structure?
> > > >
> > > > Yes.
> > > >
> > > > > Would you recommend re-referencing before ICA, especially if PCA is already used to adjust for rank deficiency?
> > > >
> > > > Yes (see above correction about the paper and recommendations). You should also interpolate bad channels before ICA (before calculating the data rank).
> > > >
> > > > > Do you have any suggestions on how to safely apply re-referencing post-ICA?
> > > >
> > > > If for some reason you must re-reference after ICA (e.g., for visualization), do one of the following:
> > > >
> > > > % Backup ICA weights before re-referencing icaweights =
> > > > EEG.icaweights; icasphere = EEG.icasphere;
> > > >
> > > > % Apply re-referencing (this alters EEG.data!) EEG = pop_reref(EEG,
> > > > []);
> > > >
> > > > % Re-apply ICA on new data manually EEG.icaact = icaweights *
> > > > icasphere * EEG.data;
> > > >
> > > > Cedric Cannard
> > > >
> > > > On Thursday, July 24th, 2025 at 12:47 PM, Tomko Settgast via eeglablist eeglablist at sccn.ucsd.edu wrote:
> > > >
> > > > > Dear EEGLAB team,
> > > > >
> > > > > I encountered a puzzling issue while applying common average referencing (CAR) after ICA, as currently recommended in the EEGlab tutorial (https://urldefense.com/v3/__https://eeglab.org/tutorials/06_RejectArtifacts/RunICA.html*issues-with-data-rank-deficiencies__;Iw!!Mih3wA!Hy4Uj5ky7Dr8niY5yJBllkYbV8sC-8D2mnYBuaJiqvb2aYgEkuwDK9M_9PyFCOjmtEKMU505X4gXGbOKz39zcB4VawTXegSozL76zYdG$ ) to address rank deficiencies.
> > > > >
> > > > > In one specific dataset, ICA components appear strongly corrupted after saving and reloading the dataset, although they looked perfectly normal before saving. This issue does not occur in other datasets processed by the same automated pipeline.
> > > > >
> > > > > Steps to reproduce:
> > > > >
> > > > > 1. Run runica and perform MARA artifact rejection.
> > > > > 2. Apply CAR using pop_reref(EEG, []) after ICA (as recommended).
> > > > > * The components looked completely normal at this point (see
> > > > > attached
> > > > > ICsBeforeSaving.gif) 3. Save the dataset using pop_saveset.
> > > > > 4. Close EEGLAB, reload the dataset, and inspect ICA components with pop_eegplot .
> > > > > * After reloading, the ICA time series look heavily distorted
> > > > > (see attached ICsAfterReload.gif)
> > > > >
> > > > > To investigate further, I saved the EEG structure as .mat immediately before saving with pop_saveset and compared it with the EEG structure after reloading the saved dataset.
> > > > > There are mismatches between the ICA matrices in these two structures - which aligns with the visual differences in the component time series.
> > > > >
> > > > > For a better understanding:
> > > > >
> > > > > * MARA did not reject components for this dataset (0 entries in gcompreject).
> > > > > * The ICA was computed with proper PCA reduction to the data rank (rank = 10), following the recommendations in Kim et al. (2023) (https://urldefense.com/v3/__https://doi.org/10.3389/frsip.2023.1064138__;!!Mih3wA!Hy4Uj5ky7Dr8niY5yJBllkYbV8sC-8D2mnYBuaJiqvb2aYgEkuwDK9M_9PyFCOjmtEKMU505X4gXGbOKz39zcB4VawTXegSozNk76r1m$ ), so ghost components are unlikely.
> > > > > * I am aware that common average re-referencing is commonly not
> > > > > recommended for datasets with a low number of channels but we
> > > > > were trying to follow the guidelines expressed by Hu et al.
> > > > > (2018;
> > > > > 10.1088/1741-2552/aaa13f)
> > > > > * The issue seems to occur only in this particular dataset - as far as I can trust my visual comparison.
> > > > > In other datasets where MARA rejected components, I did not encounter this behavior (again, only checked visually) - even though the same pipeline and re-referencing strategy was applied.
> > > > >
> > > > > After discussing the issue with ChatGPT, the suggestion came up that applying re-referencing after ICA might silently disrupt the ICA-to-data mapping, and this mismatch only becomes apparent after saving and reloading the dataset.
> > > > >
> > > > > However, what I find particularly confusing is that this issue only occurs when MARA does not reject any components - which would normally indicate better signal quality.
> > > > >
> > > > > My questions:
> > > > >
> > > > > * Can you confirm whether re-referencing after MARA-based component rejection might break the ICA structure in this way?
> > > > > * Is this a known issue with pop_reref in combination with ICA, and MARA?
> > > > > * Would you recommend re-referencing before ICA, especially if PCA is already used to adjust for rank deficiency?
> > > > > * Do you have any suggestions on how to safely apply re-referencing post-ICA without compromising the ICA decomposition?
> > > > >
> > > > > I'd be happy to share the script or provide further information if helpful.
> > > > >
> > > > > Thank you very much in advance for your time and support!
> > > > >
> > > > > Best regards,
> > > > > Tomko
> > > > >
> > > > > Tomko Settgast, MSc
> > > > > Section Intervention Psychology
> > > > > University of Würzburg
> > > > >
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