[Eeglablist] ICA on lowpass / highpass filtered data

[Krebber, Martin]

Hi all,

I am currently working on an analysis were I split the data into low and 
high frequency portions using a lowpass (cutoff 35 Hz) and a highpass 
(20 Hz) filter, respectively. The idea behind this approach is to do the 
ICA artefact rejection seperately on low and high frequency data in 
order to be better able to reject high frequency muscle artefacts and 
obtain a clearer brain signal in the gamma range.

My problem is that, especially with the highpass filtered data, ICA 
takes a very long time (roughly 5-10 times the usual) and even then the 
decomposition does not look very clean. I tried to reduce the 
dimensionality of the data (from 128 to 96) by applying the PCA 
parameter in pop_runica and it is way faster. Is it justified, or maybe 
even recommended to reduce the data dimensionality after filtering out a 
considerable portion of the signal? And if so, is there a rule of thumb 
about how much to reduce the data dimensionality?

Thanks for any suggestions!

Regards,
Martin