[Eeglablist] Open collaboration project: Multi-model Anatomical Grouping of Independent Components (MMAGIC)

[Sarvenaz Changizi]

Dear Makoto,

Thank you for sharing this invitation. Your proposal to use anatomical ROI membership as a common framework across subjects and source-localization methods sounds very interesting, and I would like to explore the possibility of contributing.

I am a PhD candidate in psychology at the Universitat de València, Spain, based at ERI-Lectura. My research focuses on reading, visual word recognition, and psycholinguistics, and involves experimental design and statistical modeling. I also have hands-on experience with EEG/ERP data acquisition and have contributed to data analysis using EEGLAB in two research projects.

Areas where I could contribute include empirical validation using EEG/ERP data, methodological input on validation design, statistical evaluation and comparison of results across approaches, and scientific interpretation, documentation, and manuscript preparation.

Could you please share more about what you have in mind for the collaboration? In particular, I would appreciate clarification on the following:

What specific tasks and priorities do you currently envisage, especially for contributors working on empirical validation and statistical evaluation?

Would validation involve datasets and analysis pipelines provided by the team, public datasets, or contributors’ own data?

What technical background would you expect for these contributions, and which tools and source-localization workflows would be used?

How would the collaboration be coordinated, and what timeline and time commitment do you anticipate? I am based in Valencia and would be interested in collaborating remotely.

What outputs are you aiming for, such as a software release or methodological paper, and how do you envisage contributor credit and authorship?

I would be happy to share my CV and discuss where my background could be most useful to the project.



Thank you for your time. I look forward to hearing more about your plans.



Best wishes,
Sarvenaz Changizi


PhD Candidate in Psychology
ERI-Lectura, Universitat de València
Valencia, Spain




> On 16 Sep 2026, at 00:02, Makoto Miyakoshi via eeglablist <eeglablist at sccn.ucsd.edu> wrote:
> 
> Hi Marjan and list,
> 
> I'd like to propose a small open collaboration project.
> 
> The idea is that we want to use anatomical ROI membership as a common
> coordinate across subjects, instead of relying on conventional IC
> clustering approach in EEGLAB STUDY. In the proposed approach, users
> specify an anatomical ROI, optionally with a distance tolerance, and ICs
> whose source models fall within that region are collected across subjects.
> In this implementation, anatomical ROI membership replaces IC clustering as
> the cross-subject correspondence rule.
> 
> Here is a prototype I originally developed for a project with Ilaria
> Berteletti, which uses standard current dipole models. Although we
> ultimately did not use it in that study, I can tell you it worked better
> than I thought.
> 
> https://urldefense.com/v3/__https://github.com/MakotoMiyakoshi/MMAGIC__;!!Mih3wA!AoKTTZuSu2CVqhzgmxuMpdjqVNFyIVR-LQZYBeLZwVRfqta0reT9bYiTM3uHevgACv2_Bf8HE91bDX8kg7QX9GoBaAI$
> <https://urldefense.com/v3/__https://github.com/MakotoMiyakoshi/AnatomicalROIGroupICA__;!!Mih3wA!AoKTTZuSu2CVqhzgmxuMpdjqVNFyIVR-LQZYBeLZwVRfqta0reT9bYiTM3uHevgACv2_Bf8HE91bDX8kg7QXcKIZKQg$ >
> 
> I cleaned the implementation to publish here and tested it on real
> ICA/DIPFIT datasets; details are available in the repository.
> 
> However, my recent work (under review) showed that more than 80% of the
> pre-qualified brain ICs by IC Label were localized deeper than
> physiologically plausible cortical source depths:
> 
> https://urldefense.com/v3/__https://www.medrxiv.org/content/10.64898/2026.01.23.26344529v2__;!!Mih3wA!AoKTTZuSu2CVqhzgmxuMpdjqVNFyIVR-LQZYBeLZwVRfqta0reT9bYiTM3uHevgACv2_Bf8HE91bDX8kg7QXDaIe1jw$
> 
> So it's time to try different cortex-constrained source estimates such as
> MNE, the LORETA family, LCMV, or other methods. The results can then be
> compared within a common anatomical ROI space. Hence MMAGIC: Multi-model
> Anatomical Grouping of Independent Components.
> 
> Marjan and I would like to develop this into a methodological/software
> project. If anyone is interested in contributing a source-localization
> method, atlas-mapping approach, validation with empirical data, or
> methodological idea, you are welcome to join us. Please let us know.
> 
> Makoto
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