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<DIV><FONT face=Arial size=2>Hi,</FONT></DIV>
<DIV><FONT face=Arial size=2>I am still in the process of working into EEGLAB
and was trying to get familiar with the ICA-variables. Especially I wanted to
recover the original data by multiplying the source activations with the mixing
matrix. As far as I understand things this should be </FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2> test = EEG.icawinv *
EEG.icaact;</FONT></DIV>
<DIV><FONT face=Arial size=2> comp = test -
EEG.data
%should be roughly zeros </FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>With a 256/257 channel dataset and a ICA
decomposition into 256 components this seems to work. </FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>My question refers to the case when I - as I use to
do in practice - first reduce the dimensionality using pca. Then the
results of " test = EEG.icawinv * EEG.icaact" cannot be equal to EEG.data.
Well, I got a bit confused about all the ICA variables (icaweights, icawinv,
icasphere) and the reduction/expansion of dimensionality with pca. </FONT><FONT
face=Arial size=2>How can I backproject the ica-source-activities from the
commandline when pca was done? </FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>The reason for doing this was that for testing
purposes I wanted to backproject single components to the scalp and visualize
them side by side in movies. I wanted to do this by zeroing out all but one row
of the EEG.icaact and then project this single activity to the scalp and plot
it. Is that correct?</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>Many thanks and my best wishes</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>Ralf.</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>P.S.: i wonder why it seems to work with the
256/257 dataset, since EEGLAB states during the ICA process that here too the
dimensionality is reduced by pca to 256 since the data are in average reference
(after referencing from cz-ref) and therefore have only rank 256 but 257
channels. or is that a different case?</FONT></DIV></BODY></HTML>