[Eeglablist] Practically reproducible results with AMICA
Grace Harvie
ghar8070 at uni.sydney.edu.au
Tue Oct 6 18:49:29 PDT 2026
Hi Scott,
I am familiar with RELICA - I actually tried to add the AMICA algorithm to relica.m, but ran into an error - when the new relamica() function (identical to relica(), but with the addition of AMICA) calls runamica15(), each instance of AMICA tries to create the same output folder and ultimately doesn't recognise the parameter "W":
[truncated for brevity]
A subdirectory or file C:\EEG\amicaouttmp\ already exists.
forrtl: The process cannot access the file because it is being used by another process.
forrtl: severe (38): error during write, unit 20, file C:\EEG\amicaouttmp\out.txt
Image PC Routine Line Source
amica15mkl.exe 00007FF7C467EE4F Unknown Unknown Unknown
amica15mkl.exe 00007FF7C4655E43 Unknown Unknown Unknown
amica15mkl.exe 00007FF7C45E40CD Unknown Unknown Unknown
amica15mkl.exe 00007FF7C507AFEE Unknown Unknown Unknown
amica15mkl.exe 00007FF7C51E8794 Unknown Unknown Unknown
KERNEL32.DLL 00007FFC1926CD87 Unknown Unknown Unknown
ntdll.dll 00007FFC1A90CAEC Unknown Unknown Unknown
Something went wrong...
No gm present, setting num_models to 1
No W present, exiting
Error using runamica15 (line 899)
Unrecognized field name "W".
Error in relamica (line 153)
parfor i=1:M
I'm sure there's probably something simple and obvious which would allow AMICA to be bootstrapped by RELICA just fine, but I wasn't sure how much time to sink into troubleshooting my hacked together version of RELAMICA.
I was unaware of those new publications with Gwen - I will have to take a look. I did, however see Makoto's visualisation of iteration by iteration changes in AMICA scalp topography (https://urldefense.com/v3/__https://youtu.be/UZKeIxU3HTY?si=NA7JoK9rUOpJveOQ__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3Vkzxra5k7y_w$ ), which similarly showed no large increases in the log likelihood after the 750th iteration and no change in the order of scalp topologies after the 1250th iteration.
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
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________________________________
From: Scott Makeig <smakeig at gmail.com>
Sent: 07 October 2026 06:13
To: Grace Harvie <ghar8070 at uni.sydney.edu.au>
Cc: eeglablist at sccn.ucsd.edu <eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] Practically reproducible results with AMICA
Grace -
You are working in a fruitful - and to date too little explored - direction, I believe.
Have you looked into Fiorenzo Artoni's RELICA <https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/XpznCwV1vMfPZn2XLsVfGpsJk5C_?domain=sciencedirect.com__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3Vkzxrcod6PA-$ > (= 'reliability of ICA components') (currently, with 123 Google Scholar citations)?
In the last version of RELICA I am aware of, we dexplictly defined two ICA decompositions - one as the standard and one to support bootstrap replications (the same number of data points, from the same dataset, only pulled at random WITH replacement (so one point might be selected > 1 times). This method has statistical support - it creates variation while never moving away from the underlying data point (scalp map) distribution.
Now, Jason's latest compile of AMICA is quite fast (especially on current machines), which should allow using AMICA for both the standard and bootstrap decompositions.
Fiorenzo's strategy in RELICA is to cluster all the resulting components - to see which show up invariably, and which do less so...
(You might want to save and/or plot - for each resulting IC cluster - pdfs of exactly WHICH data points are used in the cluster decompositions, as it might be that some sources only show up in some portion(s) of the data. I believe source space nonstationarity will be the next frontier in EEG analysis).
Now, with Gwen (see References below) we recently set out to determine the value of running more than the (heuristic) default 2,000 iterations in AMICA decompositions. Results showed that yes, the resulting ICs do slightly more independent (and have more simple, 'dipolar' scalp maps) the more iterations you let AMICA run. But at 2,000 iterations, further component changes are relatively small, and quite slow to appear. This should also depend to some extent on data length and on channel number, of course - not something we had the (computer or time) bandwidth to explore.
Feel free to write me if you are tempted to try any of this, as I might have some further thoughts, ...
[PDF] ieee.org<https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/d66pCxngwOfBqjzKJSvhErsycgMS?domain=ieeexplore.ieee.org__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3VkzxrTusBmWy$ >
Quantitative Exploration of Sufficient Data Quantities for EEG Decomposition Using AMICA<https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/HT8iCyojxQTJm3pENSQim0sxFcY7?domain=ieeexplore.ieee.org__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3VkzxrePuiNgk$ >
G Frank<https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/JgD4CzvkyVCnNz7vRSKs7js9mSxL?domain=scholar.google.com__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3VkzxrWUDLzgV$ >, SY Shirazi<https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/mz2_CANpgjCVJ58M9IQt9QsGbQgN?domain=scholar.google.com__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3VkzxrRMuEGjv$ >, J Palmer<https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/ZQ4cCBNqjlCAJNy3Vs1uyjs2MvUI?domain=scholar.google.com__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3VkzxreB6DhWi$ >… - 2025 12th …, 2025 - ieeexplore.ieee.org<https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/9YTrCZY1NqixnY0yMuwHVAsBLse1?domain=ieeexplore.ieee.org__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3VkzxrcVtSluH$ >
… Unless mentioned otherwise throughout this paper AMICA is run with a maximum of
3000 … [10] JA Palmer, K. Kreutz-Delgado, and S. Makeig, “Amica: An adaptive mixture of …
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… Additionally, AMICA can learn stationary subsets of data and fit … Unless mentioned otherwise
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On Tue, Oct 6, 2026 at 11:59 AM Grace Harvie via eeglablist <eeglablist at sccn.ucsd.edu<mailto:eeglablist at sccn.ucsd.edu>> wrote:
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://eeglab.ucsd.edu/wiki/Makoto%27s_useful_EEGLAB_code#How_to_obtain_practically_reproducible_ICA_results_(09/26/2022_added,_05/08/2024_updated) <https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/YKcTC2xMQzi6oQgAkh3S3ws5tKjJ?domain=eeglab.ucsd.edu__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3VkzxrWYZbrmc$ >
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://github.com/graematterneuro/ACTIONStudyEEGData/blob/main/AMICADecompositionReproducibility.png__;!!Mih3wA!ECb3eiORNCeyUP1u_NEtlhsE66TmRqljEcyRtGZyp6WqAlxLU16gwSe69jtFfeZAUuOzjacBKM-x3Q40quQJDJc52OiFBKxr$<https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/ZmgBCVARKgCXj8Y1lfotrVsEPY9a?domain=urldefense.com__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3Vkzxrawczg7Z$ >
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<https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/sUVpCWLVXkUxw8VvjuMukZsoaJ0f?domain=google.com__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3VkzxrYHRWZTG$ > | PO Box 412 | Westmead NSW 2145 Australia
ghar8070 at uni.sydney.edu.au<mailto:ghar8070 at uni.sydney.edu.au>
westmeadinstitute.org.au<https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/cuWhC3QNPBixYvOVmunTZEsQjimJ?domain=westmeadinstitute.org.au__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3VkzxrTyfaLii$ >
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Scott Makeig, Research Scientist and Director, Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla CA 92093-0559, http://sccn.ucsd.edu/~scott <https://urldefense.com/v3/__https://url.au.m.mimecastprotect.com/s/YOcQC4QOPEi6AxqGJhEUGXs43CBE?domain=sccn.ucsd.edu__;!!Mih3wA!DDt61xbrkRqx8Ef2RnbZQFLDgeRPvWdNS66Uz_ZifA8ANsWMk-KoCy25hiwJDYZJ6VMWII-SCITImWPxFSM3VkzxrZtND35c$ >
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