[Eeglablist] Reduction of Data Dimensionality before ICA

[Tarik S Bel-Bahar]

Greetings Martin, a few thoughts below, hope they help a little!

1. The eeglab documentation and general ICA practice both do not
recommend reducing the dimensionality of EEG data via PCA before doing
ICA. My understanding is that PCA reduces the dimensionality of the
EEG data, and that this reduction reduces the "validity" of the data,
making it less valid, whereas ICA maintains the true
dimensionality of the data. Various groups have differing opinions and
practices.
Please see also the paper below:
PLOS ONE: Independent EEG Sources Are Dipolar
www.plosone.org/.../info%3Adoi%2F10.1371%2Fjournal.pone.0030...

2. Published work with EEG and ICA does sometimes reduce data via PCA.
Note that some published articles (all findable via Google Scholar)
use PCA and then ICA, and some just ICA, and some just PCA. Perhaps
the most prominent approach is using Dien et al.'s PCA for ERP
approach with Dien's freely available sotware. See some recent
articles from Dien's or Donchin's labs, for example.

Applying Principal Components Analysis to Event-Related Potentials: A Tutorial
J Dien - Developmental Neuropsychology, 2012 - Taylor & Francis

3. Having worked with 128 and higher density data with ICA previously,
my recommendation is to avoid PCA, unless you have a very good and
principled reason to use it. Note that you cannot hurt yourself but
only learn more
by doing both types of ICA decompositions (with and without PCA-based
reduction) and comparing the results. Then you can see for yourself
the
possible differences between the ICA results you get by doing things
in two ways.




>
> It calls ICA with the following line
>
> [wts, sph] = runica( input_data, 'extended', 1, 'stop', 1e-7,
> 'maxsteps', 600, 'pca', pc);
>
> where pc is 96 minus the number of excluded and interpolated channels
> (this is mentioned earlier in the script, but without an explanation).
> As far as I understand, the 'pca' argument in that line initiates a
> reduction of the dimensionality of the data using a PCA.
>
> However, the script is intended for the analysis of 128 channel data,
> like we use in our Lab. Why does the script reduce the dimensionality of
> the data to 96 or below. Is it justified to do that and is there a rule
> for data reduction before performing an ICA. The number seems kind of
> arbitrary to me.
>
> Thanks to anybody who can help!
>
> Regards,
> Martin
>
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