[Eeglablist] Order of Channel Removal+Interpolation and ICA (removing noisy components)
Jason Palmer
japalmer29 at gmail.com
Mon Jun 8 11:46:10 PDT 2026
It should be noted that this discussion applies when entire channels are rejected.
However, it seems common that many channels are only transiently bad rather than bad for the entire recording. If you divide the data into segments, with each segment having different bad channels (or none), you could loop over the segments and interpolate the respective bad channels. The resulting data would then not be rank deficient, assuming more than one segment with different bad channels (or none).
In this case, you would want to do the advanced interpolation first, then run ICA.
Best,
Jason
-----Original Message-----
From: Jason Palmer <japalmer29 at gmail.com>
Sent: Monday, June 8, 2026 2:08 PM
To: 'Cedric Cannard' <ccannard at protonmail.com>; 'EEGLAB List' <eeglablist at sccn.ucsd.edu>
Subject: RE: [Eeglablist] Order of Channel Removal+Interpolation and ICA (removing noisy components)
Hi Cedric,
Yes, the maps are like channel time points, so interpolation works the same way.
The spherical interpolation is just a weighted sum of neighboring channels, so it can be thought of as a (tall) n x m matrix, B. Interpolating the m x N data EEG.data0 to be n x N is just (assuming no epoching),
EEG.data = B * EEG.data0;
And interpolating the m x m maps (or m x (m-1) for avg reference) is just,
EEG.icawinv = B * EEG.icawinv0;
And since EEG.data0 = EEG.icawinv0 * EEG.icaact, we have,
EEG.data = B * EEG.data0 = B * EEG.icawinv0 * EEG.icaact = EEG.icawinv * EEG.icaact;
So, you could interpolate the data directly, or interpolate the maps and multiply by EEG.icaact, to get the interpolated data.
Also, I think you get essentially the same result if you interpolate the channels before or after ICA, assuming that you do PCA with the proper dimensions and let ICA run until it converges.
So:
1. Reject k channels
2. Interpolate k channels
3. Do average reference
4. Run ICA (on the n channels) with n - k -1 PCA dimensions
Should give the same results as:
1. Reject k channels
2. Do average reference
3. Run ICA (on the n-k channels) with n - k - 1 PCA dimensions 4. Interpolate the maps and reconstruct the data
The time taken should be the same. So, the main benefit of eeg_icainterp apart from possible aesthetic preference is that it can be used if you already ran ICA on data with rejected channels and want to interpolate the maps and data without rerunning ICA.
Best,
Jason
-----Original Message-----
From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> On Behalf Of Cedric Cannard via eeglablist
Sent: Saturday, June 6, 2026 1:51 PM
To: EEGLAB List <eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] Order of Channel Removal+Interpolation and ICA (removing noisy components)
Oh yes, thanks Jason!
I wanted to do this forever but thought there may be issues with interpolation at that level because of the variance ordering of ICs in EEGLAB and at the spatial or independent sources levels. But actually, if I understand correctly, the interpolation is not applied to the ICA time series (icaact) but to the mixing matrix (icawinv), whose columns are the spatial weight map across electrodes, representing how a given source projects to each sensor. So performing the interpolation on those maps with spherical splines is geometrically appropriate for exactly the same reason it is for raw EEG: both live in the same sensor space and obey the same spatial smoothness assumptions on the scalp. So the source independence (icaact) is entirely untouched. The data is then reconstructed as icawinv_interpolated × icaact, which is just the standard linear ICA forward model with now-complete electrode coverage.
This gives such a clean pipeline now! Thanks!
Cedric
Sent with Proton Mail secure email.
On Monday, June 1st, 2026 at 6:56 PM, Jason Palmer via eeglablist <eeglablist at sccn.ucsd.edu> wrote:
> I made an eeglab function (shown below) to do the post ica channel interpolation for anyone interested. I tested it and it seems to work.
>
> -Jason
>
> function EEGout = eeg_icainterp(EEG,urchanlocs) % function EEGout =
> eeg_icainterp(EEG,urchanlocs) % % Interpolate missing channels of
> EEG.icawinv (ICA maps) and EEG data % % Inputs:
> %
> % EEG - a dataset with ICA from data with rejected / missing channels
> % urchanlocs - array of channel locations with missing channels
> %
> % Outputs:
> %
> % EEGout - dataset with ICA maps and data with rejcted /
> % missing channels interpolated
> %
>
> % get the original channel numbers
> for k = 1:EEG.nbchan
> v(k) = EEG.chanlocs(k).urchan;
> end
>
> % create fake dataset to interpolate ica maps EEGtmp = EEG;
> EEGtmp.data = EEGtmp.icawinv; EEGtmp.trials = 1; EEGtmp.pnts =
> size(EEGtmp.icawinv,2);
> EEGtmp2 = eeg_interp(EEGtmp,urchanlocs);
>
> % copy the icaact and interpolated icawinv into output EEGout = EEG;
> EEGout.nbchan = length(urchanlocs); EEGout.icawinv = EEGtmp2.data;
> EEGout.icaact = EEG.icaact;
>
> % add zero columns to icasphere where (new) interpolated data channels
> are
> EEGout.icasphere(:,v) = EEG.icasphere;
> EEGout.icasphere(:,setdiff(1:EEGout.nbchan,v)) = 0;
>
> % add data with interpolated channels
> EEGout.data = reshape(EEGout.icawinv *
> double(EEGout.icaact(:,:)),EEGout.nbchan,EEGout.pnts,EEGout.trials);
>
> % copy full chanlocs and icachansind
> EEGout.chanlocs = urchanlocs;
> EEGout.icachansind = 1:EEGout.nbchan;
>
>
>
> -----Original Message-----
> From: japalmer29 at gmail.com <japalmer29 at gmail.com>
> Sent: Saturday, May 30, 2026 9:51 PM
> To: 'Tim Curran' <tim.curran at colorado.edu>; 'Naviya Lall'
> <naviyal at iiitd.ac.in>; 'Makoto Miyakoshi' <mmiyakoshi at ucsd.edu>
> Cc: eeglablist at sccn.ucsd.edu
> Subject: RE: [Eeglablist] Order of Channel Removal+Interpolation and
> ICA (removing noisy components)
>
> I forgot the final step:
>
> 8. Replace EEG.data in the ica dataset with the interpolated
> EEG.icawinv * EEG.icaact
>
> Jason
>
> -----Original Message-----
> From: japalmer29 at gmail.com <japalmer29 at gmail.com>
> Sent: Saturday, May 30, 2026 9:40 PM
> To: 'Tim Curran' <tim.curran at colorado.edu>; 'Naviya Lall'
> <naviyal at iiitd.ac.in>; 'Makoto Miyakoshi' <mmiyakoshi at ucsd.edu>
> Cc: eeglablist at sccn.ucsd.edu
> Subject: RE: [Eeglablist] Order of Channel Removal+Interpolation and
> ICA (removing noisy components)
>
> Hi all,
>
> It seems that a major concern here is to keep all the channel locations in the dataset. However, I think this is possible without interpolating first.
>
> Since spherical interpolation is just a linear combination of neighboring channels, the resulting dataset will be rank deficient, and ICA should remove the redundant dimensions by PCA. PCA reduction has been shown to be detrimental by Artoni.
>
> Alternatively, you can interpolate the component maps of the mixing matrix. However, as there is no function currently to do this, the process is a bit roundabout. I and a colleague in Germany have done this successfully in the past.
>
> The process is:
>
> 1. Save the full channel locations
> 2. Reject bad channels
> 3. Run ICA
> 4. Create fake dataset with the EEG.icawinv as EEG.data, adding back in zero rows for the rejected channels with the original channel locations
> 5. Run eeg_interp on the fake dataset to get full icawinv
> 6. Copy full icawinv and full channel locations into the ica dataset
> 7. Add zero columns to the icasphere matrix corresponding to the bad channels.
>
> Best,
> Jason
>
> -----Original Message-----
> From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> On Behalf Of Tim
> Curran via eeglablist
> Sent: Friday, May 29, 2026 2:50 PM
> To: Naviya Lall <naviyal at iiitd.ac.in>; Makoto Miyakoshi
> <mmiyakoshi at ucsd.edu>
> Cc: eeglablist at sccn.ucsd.edu
> Subject: Re: [Eeglablist] Order of Channel Removal+Interpolation and
> ICA (removing noisy components)
>
> Thanks for the replies. Very helpful!
> best
> Tim
>
>
> From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> on behalf of
> Naviya Lall via eeglablist <eeglablist at sccn.ucsd.edu>
> Date: Friday, May 29, 2026 at 10:25 AM
> To: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> Cc: eeglablist at sccn.ucsd.edu <eeglablist at sccn.ucsd.edu>
> Subject: Re: [Eeglablist] Order of Channel Removal+Interpolation and
> ICA (removing noisy components)
>
> [External email - use caution]
>
>
> Thank you for these responses, they are incredibly helpful. Brief
> follow up
> questions-
> 1. Should I use script to first save the number of the channels, then
> remove the noisy ones and then interpolate the ones removed *OR* can I
> just tell EEGLAB the noisy channels and then interpolate without
> removing like
> this:
> bad_idx = find(ismember({EEG.chanlocs.labels}, {'F10'})); % EEG =
> pop_interp(EEG, bad_idx, 'spherical'); EEG = eeg_checkset(EEG);
> [ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG, CURRENTSET);
>
> 2. If I interpolate the noisy channels without removing them, do I still run the PCA adjusted ICA? like so- n_chans = EEG.nbchan; data_rank = rank(double(EEG.data(:,:))); % or just:
> n_chans - n_interpolated
> EEG = pop_runica(EEG, 'icatype', 'runica', 'extended', 1, 'pca',
> data_rank);
>
> 3. Dr. Miyakoshi- If I follow this strategy of interpolating before ICA, would it be sensible to cite the paper you referred to?- Kim H, Luo J, Chu S, Cannard C, Hoffmann S and Miyakoshi M (2023) ICA’s bug: How ghost ICs emerge from effective rank deficiency caused by EEG electrode interpolation and incorrect re-referencing. Front. Sig. Proc. 3:1064138. doi:
> 10.3389/frsip.2023.1064138
>
> Thank you!
>
> Best regards,
> Naviya
>
> --
> Naviya Lall
> Junior Research Fellow
> Cognitive Science Lab
> IIIT Delhi
> naviyalalluni.wixsite.com
> <https://urldefense.com/v3/__https://nam10.safelinks.protection.outloo
> k.com/?url=https*3A*2F*2Furldefense.com*2Fv3*2F__https*3A*2F*2Fnaviyal
> alluni.wixsite.com*2Fnaviyalall__*3B!!Mih3wA!FQyTrO4VCQzKiAaHQYBhwg-G7
> LrmGyGdl2DOVzeIYwL4krOhTWwF7h59XTGli76LuO9fipK7WDEdwyD-E89CpGG1*24&dat
> a=05*7C02*7Ctim.curran*40colorado.edu*7C1beb1b4b14744df80a1408debd9ee8
> 6a*7C3ded8b1b070d462982e4c0b019f46057*7C1*7C0*7C639156687403992007*7CU
> nknown*7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiO
> iJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ*3D*3D*7C0*7C*7C*7C&sdata=DEhskU0
> UGYxm4gtRt1NWvwbHYYOBzFSsvCxepjcfsyg*3D&reserved=0__;JSUlJSUlJSUlJSUlJ
> SUlJSUlJSUlJSUlJSUlJQ!!Mih3wA!BFiVpl-PLlTmBo4LrSj6ULfz94V4okiXi8N_cWD2
> oi_ajV9ss9GGx4QBsIWriMFC9p03a7scPz5eTI2xr9T1Jx_5W3QweA$ >
>
>
> On Fri, May 29, 2026 at 3:18 AM Makoto Miyakoshi via eeglablist < eeglablist at sccn.ucsd.edu> wrote:
>
> > Hi Marshid and Tim,
> >
> > Thank you for your comments. It's my honor to go against Claude's advice!
> >
> > In fact, I recognize Claude's workaround works fine for a specific purpose:
> > it uses ICA purely as an electrode signal cleaner at the cost of
> > IC-electrode correspondence i.e., your EEG.icawinv will be
> > invalidated. As a result, certain types of analyses become
> > impossible, such as envelope-topography (envtopo) at the group-level
> > analysis, because matrix dimensions do not match across datasets.
> >
> > Here is the step by step examination.
> >
> > 1. We all agree that bad channels need to be excluded before ICA.
> > Suppose that we reject n channels here.
> >
> > 2. We run ICA on this channel-reduced data. As a result, the reduced
> > number of electrodes are registered to your ICA-generated matrices
> > (EEG.icaweights, EEG.icasphere, EEG.icawinv, EEG.chaninds).
> >
> > 3. After ICA, you interpolate rejected channels to recover your
> > original EEG.nbchan. Technically, you can do it. Probably you see no
> > error message in EEGLAB. However, this interpolation does not take
> > care of ICA-generated matrices (as far as I know). As a result, you
> > can't perform IC rejection for electrode signal cleaning because the
> > number of channels of your scalp recording and of your ICA-generated
> > matrices do not match. You do see an error message here.
> >
> > 4. The easiest way (i.e., without developing a reasonable workaround
> > and publishing it a dedicated technical paper to address this
> > specific
> > problem) to avoid this problem is to perform channel interpolation
> > BEFORE ICA so that all the original electrodes are registered to
> > ICA-generated matrices so that you can perform IC rejection etc.
> > after ICA. The drawback is that you need to use PCA (or whatever)
> > dimension reduction to run full-rank ICA
> > decomposition: see my ICA's bug paper for detail. Spline channel
> > interpolation is the most dangerous process for ICA because it does
> > not cause a clean rank deficiency due to its nonlinearity. If the
> > smallest eigenvalue is < 1E-6, ICA starts to generate 'ghost ICs',
> > even though Matlab's rank() function says 'the data are full ranked'!
> > I credit Sven Hoffmann for finding this threshold.
> >
> > To conclude, my suggestion guarantees group-level data compatibility
> > between scalp and IC sources at the cost of the use of PCA dimension
> > reduction in ICA, and Claude's suggestion works fine only for
> > channel data analysis.
> >
> > Makoto
> >
> > On Thu, May 28, 2026 at 9:24 AM Tim Curran <tim.curran at colorado.edu>
> > wrote:
> >
> > > Hi Makoto,
> > > I have been playing with Claude Code, and using the latest
> > > Claude.md file with EEGLAB.
> > >
> > > Claude.md file says:
> > > "Typical pipeline position: clean_rawdata (removes bad channels)
> > > -> re-reference -> ICA -> ICLabel -> remove components ->
> > > **interpolate** -> re-reference (again, optional) -> epoch.”
> > >
> > > You do not agree with eeglab’s Claude.md file or maybe I am
> > > missing something?
> > >
> > > thanks
> > > Tim
> > >
> > >
> > >
> > > *From: *eeglablist <eeglablist-bounces at sccn.ucsd.edu> on behalf of
> > Makoto
> > > Miyakoshi via eeglablist <eeglablist at sccn.ucsd.edu>
> > > *Date: *Wednesday, May 27, 2026 at 5:41 PM
> > > *To: *eeglablist at sccn.ucsd.edu <eeglablist at sccn.ucsd.edu>
> > > *Subject: *Re: [Eeglablist] Order of Channel Removal+Interpolation
> > > and ICA (removing noisy components)
> > >
> > > [External email - use caution]
> > >
> > >
> > > Hi Naviya,
> > >
> > > - My main question is if I should perform interpolation before
> > > ICA or after ICA?
> > >
> > > Do it BEFORE ICA.
> > > If you perform channel interpolation after ICA, your EEG.icasphere
> > > does
> > not
> > > have columns for the added channel.
> > >
> > > Makoto
> > >
> > > On Tue, May 26, 2026 at 2:19 PM Naviya Lall via eeglablist <
> > > eeglablist at sccn.ucsd.edu> wrote:
> > >
> > > > Hello all,
> > > >
> > > > My name is Naviya and I work with EEG data in a lab in Delhi,
> > > > India. I
> > > have
> > > > a question about EEG data channel interpolation and its order in
> > > > preprocessing pipelines.
> > > >
> > > > - We record data with high density EEG (128 channels).
> > > > - I tried to perform ICA without removing any channels and it
> > > > was
> > > giving
> > > > me noise heavy components (I use ICA to remove noisy
> > > > components of
> > > eye,
> > > > heart, muscle etc.)
> > > > - I just want to remove 2-4 channels in some participants or certain
> > > > sessions.
> > > > - The GUI has very easy direct interpolation
> > > > steps- Tools>Interpolate>select from data channels which skips the
> > > > "Removal" step altogether.
> > > > - In code I feel that it would be easier to "remove" the noisy
> > > > channel(s) and use this - original_chanlocs = EEG.chanlocs;
> > > > (to
> > save
> > > > original locations) and then EEG = pop_interp(EEG,
> > original_chanlocs,
> > > > 'spherical');
> > > > to interpolate the removed data- *Is that right?*
> > > > - My main question is if I should perform interpolation
> > > > before ICA
> > or
> > > > after ICA?
> > > > - I read through some older exchanges on EEGLABLIST Archive
> > > > from
> > 2015,
> > > > 2017, 2023 and 2025 however I am still unsure of the ideal order of
> > > > performing interpolation.
> > > > - My logical thought is to remove + interpolate before
> > > > running ICA
> > so
> > > > that the rank and number of components generated is not
> > > > affected but most
> > > > people advise to remove channel, then ICA and then interpolate.
> > > > - Please advise on the method of channel
> > > > removal+interpolation and
> > the
> > > > order of channel interpolation and ICA?
> > > >
> > > >
> > > > Thank you so much.
> > > >
> > > >
> > > > Best regards,
> > > > Naviya
> > > >
> > > > --
> > > > Naviya Lall
> > > > Junior Research Fellow
> > > > Cognitive Science Lab
> > > > IIIT Delhi
> > > > naviyalalluni.wixsite.com <
> > > >
> > >
> > https://urldefense.com/v3/__https://nam10.safelinks.protection.outlo
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> > https://urldefense.com/v3/__https://nam10.safelinks.protection.outlo
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> > ok
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