[Eeglablist] Applying ICA weight matrix on another dataset

[Shou, Guofa]

I also has the same concern about it. 
If we assume the brain sources are stable, then we can claim the EEG.icawinv and EEG.icaweight should be same for two dataset (e.g., the same data with different filters). 
However, the two dataset have different whitening matrix (EEG.icasphere). So my additional question is whether we need to replace the EEG.icasphere with the current dataset, especially for the case that less number of ICs decomposed compared to number of channels?
The EEG.icasphere which calculated with ICA is a identify matrix if I choose less number of ICs relative to number of channels.

Shou


Guofa Shou PhD
Postdoc research associate,
Computational Imaging Laboratory,
University of Oklahoma
3100 Monitor Ave. Suite 280
phone: 405-245-9382
________________________________________
From: eeglablist-bounces at sccn.ucsd.edu [eeglablist-bounces at sccn.ucsd.edu] on behalf of Maarten De Schuymer [maartendeschuymer at gmail.com]
Sent: Thursday, February 14, 2013 1:51 PM
To: eeglablist at sccn.ucsd.edu
Subject: [Eeglablist] Applying ICA weight matrix on another dataset

Dear list,

I have a question concerning the role of the sphering matrix (which decorrelates the channels) in the rather common scenario where I compute an ICA on one version of a dataset, but then apply the ICA results to another version of the same data (e.g. epoched vs. continuous, filtered vs. unfiltered).
To remove artifacts in my study, I compute the ICA on high-pass filtered (e.g. 1 Hz) data, because this results in much better ICA decompositions. However, I would like to apply the results of this ICA to my original, unfiltered version of the same dataset, because would like to keep slow potentials (< 1 Hz) in the data. After running ICA on the filtered data, I save both EEG.icaweights and EEG.icasphere.

When I now apply the ICA weight matrix to the original data it is unclear to me which sphering matrix needs to be used.
Should I (A) also import the ICA sphering matrix from the filtered data or (B) recompute the sphering matrix (cmd: sphere(EEG.data)) for the original unfiltered data and consequently use that one. Both possibilities result in different outcomes since sphering matrices are different for both versions of the datasets. Which of these possibilities are recommended and more importantly, why exactly?
A related question concerns the exporting-importing of the weight matrix in the GUI of EEGLAB. When exporting weights, a single exported file contains the combined weight*sphering matrix. However, when importing, two different files need to be imported, i.e. both weight matrix and sphere matrix separately. This does not seem practical. Or is there a rationale behind this distinction between import and export?

Thanks for any input on this,

Best
Maarten De Schuymer