[Eeglablist] Practically reproducible results with AMICA

Sun Oct 11 20:33:08 PDT 2026

Hi Grace,

Just a thought. Nowadays, AIs check data reproducibility at the level of
bitwise identical or not, at least by default. In this sense, we cannot
expect ICA to produce bit-by-bit identical results no matter how many
iterations we set.

Makoto

On Wed, Oct 7, 2026 at 4:26 PM Cedric Cannard via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Hi,
>
> I don't want to add more compute time, but I'd be curious to see how these
> results are affected by filtering (we all know highpass is crucial and
> gives very different results if you remove low-freq drift or not, that's in
> Winkler et al. 2015, but I'd be curious about lowpass filtering between 30
> hz vs lowpass 45 hz vs just a notch filter at power line freq), and
> different sample rates (e.g. 250 vs 500 hz to keep it simple). I would
> suspect the reproducibility would increase with lower filter cutoff (e.g.
> 30 hz since otherwise you get more line noise related components) and
> higher sample rates.
>
> Good question about data cleaning, that likely also affects the
> convergence of those types of results. I'd recommend ensuring always
> highpass filter at 1 Hz and a conservative ASR cleaning (e.g. at a
> threshold around 100 (depending on the data quality, the most important is
> that it doesnt remove good brain activity like alpha or ocular artifacts so
> they are separated properly). Of course before ASR and ICA, make sure to
> remove bad channels (e.g., with clean_rawdata plugin or clean_channels
> function directly), and make sure to input the effective data rank when
> running ICA (see Kim et al. 2023 if you haven't already), I can't remember
> if that is done automatically under the hood or not, but I do remember the
> input option is available for sure. Jason can confirm.
>
> I would also like to see comparison of results with different
> re-referencing schemes (CAR, REST, Surface Laplacian) but with 26 channels,
> you're pretty limited in options here. I'd recommend doing this project on
> at least 64 electrodes dataset (and probably compare also resting state
> with other task-based datasets, where reproducibility will probably vary
> differently). I would suspect CAR and REST may converge similarly (although
> I'd love to see the results) and Surface Laplacian to be less reproducible
> (you could drop that one to save compute time since most people don't do
> that much anymore).
>
> Good luck!
>
>
> Cedric
>
>
> Sent with Proton Mail secure email.
>
> On Wednesday, October 7th, 2026 at 12:18 AM, Grace Harvie via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
> > Hi Jason,
> >
> > Thanks for the helpful input!
> >
> > I was using the default iterations of 2000, but experimented with
> iterations of up to 15000, as I noticed that the algorithm would hit the
> maximum number of iterations. When set to 15000 iterations, the first run
> performed approximately 12000 iterations before exiting because of the
> likelihood increase (<1e-9), while the second run performed 7407 iterations
> before exiting for the same reason.
> >
> > For that last run of 7407 iterations, LL = -3.1371323664, however I did
> notice on both of the 15000 iteration runs, the nd didn't seem to decrease,
> hovering between 2.8718e-5 and 8.0337e-6 for the last 2000 iterations of
> that last run, like you describe.
> >
> > I should probably describing the data I'm working with - 2 minute
> resting state recording from 26 cephalic channels sampled at 500Hz, however
> the participants were paediatric/adolescent ADHD patients, so there are
> sometimes quite significant movement/muscle artifacts present in the
> recordings. I've tried to strike a balance between adequate cleaning
> (highpass filtering, cleanline, ASR) prior to allow high quality ICA
> decomposition and not over preprocessing the data (As per Arno's 2023
> article, "EEG is better left alone",
> https://urldefense.com/v3/__https://doi.org/10.1038/s41598-023-27528-0__;!!Mih3wA!HRj9wPvczBfozBYg895WdVhu1e5D2_L1MLwYWRWA1aaku0I9KQ-KDM_aJHGiVucuVMBVj8HnJcdsbmZh3dNvG-UK1PIeeZCQ$
> ).
> >
> > It sounds like the quality of the data might be prohibiting a high
> quality decomposition, which concerns me, because the EEG recording I've
> been testing this on is one of the cleaner in the dataset.
> >
> > There are definitely many common component maps across the multiple
> runs, so I might play around with matcorr.
> >
> > Many thanks,
> > Grace Harvie
> >
> > PhD Candidate, Faculty of Medicine and Health, University of Sydney
> >
> > Research Assistant, Brain Dynamics Centre, Westmead Institute for
> <https://urldefense.com/v3/__https://www.google.com/maps/search/tmead*Institute*for?entry=gmail&source=g__;Kys!!Mih3wA!E8LHBPg8vXrXElWMgGL-mUgOOzwIsJzKI_BZw10bm1qpJwShvOY4C8exh0iaih_ezc9QF0E996lRB_72GI89N8Zfprg$ >
> Medical Research
> >
> > 176 Hawkesbury Road | PO Box 412 | Westmead NSW 2145 Australia
> > ghar8070 at uni.sydney.edu.au
> > westmeadinstitute.org.au
> >
> >
> > This email is private and confidential to the intended recipient. If you
> are not the intended recipient, please do not copy it, circulate it or take
> any action in reliance on it. Kindly notify me that it has been misdirected
> and then delete it. Thank you.
> >
> > ________________________________
> > From: Jason Palmer <japalmer29 at gmail.com>
> > Sent: 07 October 2026 07:38
> > To: Grace Harvie <ghar8070 at uni.sydney.edu.au>; eeglablist at sccn.ucsd.edu
> <eeglablist at sccn.ucsd.edu>
> > Subject: RE: [Eeglablist] Practically reproducible results with AMICA
> >
> > Also the "nd" in the output is the average weight change and should be
> less
> > than 1e-5 or so. The issue is whether the algorithm has fully converged.
> > Ideally the LL should be increasing by less than some small number like
> > 1e-9, and the weight change should go to zero, or nd < 1e-7 e.g.
> Sometimes
> > the nd may actually only get to 1e-5 or so and "hover" if there is noise
> > causing an unstable optimum.
> >
> > The components are arranged in variance order, where v =
> sum(EEG.icawinv.^2)
> > .* var(EEG.icaact(:,:)'). If the IC variances are close the order may
> change
> > while the component (map) doesn't change. The matcorr function can match
> the
> > columns of the icawinv map matrices.
> >
> > Best,
> > Jason
> >
> > -----Original Message-----
> > From: Jason Palmer <japalmer29 at gmail.com>
> > Sent: Tuesday, October 6, 2026 1:33 PM
> > To: 'Grace Harvie' <ghar8070 at uni.sydney.edu.au>;
> eeglablist at sccn.ucsd.edu
> > Subject: RE: [Eeglablist] Practically reproducible results with AMICA
> >
> > Hi Grace,
> >
> > The number of iterations should be the main factor. Are you using the
> > default 2000? I usually do 2500, but if you are testing reproducibility,
> > then I would recommend 5000 max iterations. (It's a good idea to set
> > writestep or Write Out iterations to 500 for speed purposes, and set
> > 'do_opt_block' or optimize block size to checked. And the maxthreads to
> > whatever your system can handle, like 4 if you have 8 cores e.g. This is
> all
> > just for speed.) Also, you can check the final value value of LL, which
> is
> > what AMICA is maximizing.
> >
> > Best,
> > Jason
> >
> > -----Original Message-----
> > From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> On Behalf Of Grace
> > Harvie via eeglablist
> > Sent: Monday, October 5, 2026 7:46 PM
> > To: eeglablist at sccn.ucsd.edu
> > Subject: [Eeglablist] Practically reproducible results with AMICA
> >
> > Hi All,
> >
> > I'm back with another AMICA question - this time regarding obtaining
> > practically reproducible decompositions with AMICA.
> >
> > I was able to obtain consistent and reproducible results with extended
> > INFOMAX by utilising the parameters described by Makoto's here:
> >
> https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/NXddCBNqjlCAJWVG7fzfyjs25FIy?domain=eeglab.ucsd.edu__;!!Mih3wA!HRj9wPvczBfozBYg895WdVhu1e5D2_L1MLwYWRWA1aaku0I9KQ-KDM_aJHGiVucuVMBVj8HnJcdsbmZh3dNvG-UK1Mj2Oevp$
> > ctically_reproducible_ICA_results_(09/26/2022_added,_05/08/2024_updated)
> >
> > While Makoto comments that the default parameters for AMICA are much more
> > refined and seem to produce similarly stable, replicable results, I have
> > observed variable results over multiple runs with AMICA - please see the
> top
> > 7 ICs for ten AMICA runs on the same dataset here:
> >
> https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/6Z8yCD1vlpTMrYB95t5hG6sjClvb?domain=urldefense.com__;!!Mih3wA!HRj9wPvczBfozBYg895WdVhu1e5D2_L1MLwYWRWA1aaku0I9KQ-KDM_aJHGiVucuVMBVj8HnJcdsbmZh3dNvG-UK1D1WXMYn$
> >
> GData/blob/main/AMICADecompositionReproducibility.png__;!!Mih3wA!ECb3eiORNCe
> >
> yUP1u_NEtlhsE66TmRqljEcyRtGZyp6WqAlxLU16gwSe69jtFfeZAUuOzjacBKM-x3Q40quQJDJc
> > 52OiFBKxr$
> >
> > Additionally, on one run (Run 8), I received a warning about the ICA
> > activities and weight matrix not matching as soon as AMICA finished. I'm
> not
> > sure why I got that warning, especially since EEG.icaact and
> EEG.icaweights
> > data structures appear to each have the correct number of values. I'm
> also
> > not sure if it's relevant to the issue at hand, but I thought it was
> > probably a good idea to mention it anyway.
> >
> > Since Makoto showed that INFOMAX reproducibility could be improved by
> > increasing the number of iterations and lowering the learning rate, I
> tried
> > to similarly tweak these parameters in AMICA, however it didn't seem to
> > help.
> >
> > Does anyone have any recommendations to help improve reproducibility of
> > AMICA results?
> >
> > Many thanks,
> > Grace Harvie
> >
> > PhD Candidate, Faculty of Medicine and Health, University of Sydney
> >
> > Research Assistant, Brain Dynamics Centre, Westmead Institute for Medical
> > Research
> >
> > 176 Hawkesbury Road | PO Box 412 | Westmead NSW 2145 Australia
> > ghar8070 at uni.sydney.edu.au westmeadinstitute.org.au
> >
> >
> > This email is private and confidential to the intended recipient. If you
> are
> > not the intended recipient, please do not copy it, circulate it or take
> any
> > action in reliance on it. Kindly notify me that it has been misdirected
> and
> > then delete it. Thank you.
> >
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> >
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