[Eeglablist] How does transferring ICA matrixes between same-subject data sets affect further processing?

[duncan.huizinga at gmail.com]

Hi all,

I have been discovering about the wonders of EEG research and all the impressive possibilities with EEGLab, but face one challenging question I have not been able to answer so far, which likely in part because I am still not completely certain about the precise nature of the ICA algorithm. (This might also mean the title question is somewhat non-sensical; In that case please forgive my ignorance.) Let’s assume I do understand, and please correct me whenever my reasoning is wrong:

I recorded EEG data (64 channel) from a number of subjects in various bodily states, including sitting, standing, (fast) walking and cycling. The active conditions obviously add a considerable amount of noise to the data, in the form of excessive EMG, cable sway, sensor displacement and slow drifts as a result of sweat. I am not directly looking into any locomotion related brain activity, merely its effects on other cognitive processes. 
In order to be able to do succesful source separation and localization, I figured that one could run ICA on the recordings from the passive conditions – assuming that the neural sources of the signals that are of interest to us remain stable – and then use the resulting weight and sphering matrixes for the active conditions as well (in a similar fashion as I have seen it suggested with high pass filtered data in Makoto’s preproc. pipeline). 

This is where I get lost, because I am not sure what happens with the parts of the signal that did not go into ICA – it seems like this is unaccounted for by any independent component? Would this mean that if I decided to subtract all IC’s from the data to which the weight and sphering matrixes were transferred, there would be a remaining signal consisting of all the activity that lacked in the passive conditions?
I am assuming this also means that for example, any neck muscle activity particular to the active conditions cannot be subtracted from the signal in that data, for the donor data set did not contain this. 
And does this change if there was some minor neck muscle EMG signal present in the donor set, compared to major activity in the receiving one? I guess this must at least help doing a better source localization of such EMG signals, although that procedure in itself may be better left for another discussion.

Thanks a lot in advance!

Regards,
Duncan Huizinga




On a side note: Some of you may have noticed browsing through the EEGLablist online archives can be a bit of a mess, as some text refuses to reflow. I made a very basic Stylish-script (useful addon for Firefox and Chrome) which should solve that issue, and it changes the font into something nice (which is easily changed into your own preferences by the way; just look into the code).
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