[Eeglablist] Practically reproducible results with AMICA

[Grace Harvie]

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) 

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$ 

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


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