[Eeglablist] ICA clustering

[Adam Grinberg]

Thank you, Scott, for the detailed response!

Although I was not considering decomposing my data twice (a second time on each condition separately), I will try to do this if only to explore what it will produce.

On a rather simple matter, I was wondering if you could help me with plotting clusters of IC dipoles. The default property plots each cluster in a separate plot (i.e., when using the GUI). However, I wish to plot several cluster on the same plot (Each cluster in a distinct colour; individual sources as small dots and centroids slightly larger). I tried
Using:
STUDY = std_dipplot(STUDY,ALLEEG, 'clusters',’all’, 'mode', 'multicolor', 'groups', 'on');

…but this gives me a completely different figure (you probably know which one as you were one of the authors for the function 😊).

(I hope it’s not too much to ask).

Kind regards,

/Adam
______________________________________________________________________________________
Adam Grinberg, PT, PhD
Department of Community Medicine and Rehabilitation, Physiotherapy
Umeå University
Umeå, Sweden
Tel: +46 (0) 90 786 96 30

From: Scott Makeig <smakeig at gmail.com>
Sent: 24 May 2023 17:39
To: Adam Grinberg <adam.grinberg at umu.se>; Matthew Wisniewski <mgwisniewski at ksu.edu>; Johanna Wagner <johanna-ix at gmx.de>; Fiorenzo Artoni <fiorenzo.artoni at gmail.com>; Shirazi, Yahya <syshirazi at ucsd.edu>
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] ICA clustering

Adam -

Performing ICA decomposition on your entire subject task data is not so misguided as you seem to think. I like to say that ICA identifies source activities (we call them activations) by removing from the data all the other (max independent) sources. It does this by learning the inverse weights (runica) or the source maps themselves (AMICA) - they are just inverses of each other. Most IC sources of interest - in particular distinct non-brain sources (eye movements, scalp muscle activities, line noise artifact) are in common to both task conditions (unless they are very different from each other). Therefore, we routinely decompose the whole task data and expect that ICA will use the larger training data to separate the components (and learn the component scalp projection maps) more exactly.

Now, one way to see whether source space non-stationarity is created by change in task conditions is to run AMICA decomposition on the whole data to give two models - which compete with each other during decomposition for data points, thereby separating the data into two models. A post-AMICA tool will plot the log likelihood of each measure through the dataset (using some smoothing length you can set). Does this plot show that each condition receives its own model?   Very likely not, or only for a few subjects, as the models compete for each data frame (time point) separately. Unfortunately, we have not yet found a good method for learning what exactly produces the model separation - different brain or non-brain sources, different activity patterns? ...

A second possibility is to a) decompose the whole data, b) remove interpretable non-brain sources, then separately decompose the two dimension-reduced portions corresponding to the two conditions?  Do they produce different brain components of interest?  And how do you know they are different? (Here you might use a repeated decomposition approach such as RELICA uses).   Though this may require a lot of programming to be made statistically sound, simply trying it as an exploratory step should not be difficult...

Scott Makeig

On Wed, May 24, 2023 at 10:57 AM Adam Grinberg via eeglablist <eeglablist at sccn.ucsd.edu<mailto:eeglablist at sccn.ucsd.edu>> wrote:
Hi,
I am interested in performing source reconstruction on independent components.

I have epoched data of ERP experiment, with two conditions (Both conditions are on the same dataset). I already ran ICA on each dataset (i.e., each participant) and now wish to create an EEGLAB study for all participants. I first tried to create a study and add "code" as an independent variable in the study design, to differentiate between the trials. EEGLAB just ignores this, it seems... The result is that the PCA clustering is performed on all trials regardless of condition. Not very useful.

Another option is to divide the datasets into two, a separate dataset for each condition. This is actually a requirement when creating a "simple ERP STUDY".
Alternatively, I can create one study for each condition.

However, when I separate the conditions into two datasets, the ICA weights from the original dataset are retained. Therefore, when I cluster the components, the plots look the same for both conditions. Does anyone have any insights? How can I solve this problem?

Thank you in advance.

/Adam
______________________________________________________________________________________
Adam Grinberg, PT, PhD
Department of Community Medicine and Rehabilitation, Physiotherapy
Umeå University
Umeå, Sweden
Tel: +46 (0) 90 786 96 30

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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