[Eeglablist] Order of Channel Removal+Interpolation and ICA (removing noisy components)
japalmer29 at gmail.com
japalmer29 at gmail.com
Sat May 30 18:50:54 PDT 2026
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
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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 <
> > >
> >
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