[Eeglablist] value of PCA pre-processing before running ICA on EEG data?

[Anish Patel]

Dear EEGLab/ICA experts,

I am currently working with 128-channel EEG data (sampling rate of 500 Hz 
and epoch length of 10 seconds) with the intention of running ICA on it to 
try and elucidate the original sources.

After low-pass filtering the raw data at 70 Hz, I started playing around 
with PCA as a pre-processing technique to try and reduce the dimensionality 
of the data (since I don't think I have enough data points - 500 * 10 = 
5000 - to accurately extract 128 independent components) before running ICA. 
A few things I was wondering about:

[1] How do I know how many dimensions to reduce the data to?  So far, I have 
been choosing to keep just enough principal components such that ~ 99% of 
the variance is retained (but I only picked that value arbitrarily), which 
usually halves the dimension of the data.

[2] Once I reduce the dimensionality of the data with PCA to 'p' 
uncorrelated components, how many independent components 'c' do I choose to 
extract?  Should c=p?

[3] Is there any difference between: [a] running ICA and extracting (say) 60 
components from the original raw data, and [b] first running PCA to reduce 
the raw data to the largest 60 principal components, and then running ICA 
and extracting 60 independent components from the pre-processed data?  If 
there is a difference, which is the more appropriate method?

If anyone can offer any insight into these questions, it would be greatly 
appreciated.  So far, I have just been picking arbitrary values for 'c' and 
'p' (ie. trial and error) and hoping for things to work out.  I am really 
stumped about question [3] though... I don't know which method is better, or 
even if it makes a difference which I choose.

Thanks in advance for any assistance!

- Anish