[Eeglablist] Practically reproducible results with AMICA

Tue Oct 6 18:39:00 PDT 2026

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 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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