[Eeglablist] Lack of convergence in AMICA solution

[Scott Makeig]

Farhan -

You raise good questions. In fact, I know of no systematic assessment of
the stability of multi-model AMICA decomposition. I suspect, in your case,
the following:
- Perhaps there were no meaningful state transitions in your data, thus the
'extra' multiple models latched onto various noise data features.
   See Hsu et al. 2018 on interpretation of multiple models in datasets
involving state changes.
- Perhaps the dimension of your recording (10-12 channels) was insufficient
to create a stable decomposition -- more independent sources than
channels...
   What ASR processes did you employ? Data correction?
- How did you see that the solutions were 'very different'?  Different time
domains for the 3 models? Different IC maps?
   The post-AMICA menu invokes tools to plot the domains of the different
models.
- One should expect that most ICs should be shared by all models (e.g., ICs
accounting for eyeblink artifact? etc.).

Scott Makeig

On Sun, Sep 27, 2020 at 10:33 PM Farhan Ali (Asst Prof) <
farhan.ali at nie.edu.sg> wrote:

> Hi EEG experts,
>
> I'm hoping to use AMICA to assess brain dynamics EEG dataset.
>
> I'm running into problems of the AMICA model solutions being very
> different across different runs of the exact same input data. This suggests
> that the estimation is not converging to a global solution. The dataset is
> as follows: 10-12 channels (after ASR), ~100k samples (128 Hz). The AMICA
> run on a local computer: 5 states/models, 3 generalized Gaussian mixtures
> for each component, with default settings for everything else. I tried
> changing all sorts of settings including running for over 20,000 iterations
> (took hours). Nothing helped with convergence across runs.
>
> The only thing that helped was to do an initial ICA decomposition, choose
> a subset of components (high probability of brain signals), project back to
> channel space, then run AMICA on this data with extended infomax with 1
> Gaussian (instead of generalized Gaussian). Then I get convergence in AMICA
> solutions across runs. However, within each run, all models returned all
> have almost identical IC scalp topographies with only differences in
> ordering and Gaussian PDFs. I suppose doing an initial ICA assuming
> stationarity reduces dimensionality of the data and effectively makes the
> subsequent AMICA models share ICs? But how then do I interpret the
> different models? Do the ordering differences and the variance in the
> Gaussian PDFs become the basis for interpreting the models since the IC
> scalp topographies are identical?
>
> Would anyone be able to give some insights? Thanks.
>
> Regards,
> Farhan
> National Institute of Education, Singapore
>
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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