[Eeglablist] Lack of convergence in AMICA solution

[Farhan Ali (Asst Prof)]

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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