[Eeglablist] ICA and dipfit: high residual variance

[Tarik S Bel-Bahar]

Hello Giovanni, some notes below for you that should help a little.
Let the list know of your future success for
dipfit-residual-variance-reduction!





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Your main question should probably be "are my ICs and the dipfit
results" computed correctly and accurate ? After you're sure about
that, then  think about "ways" to "decrease" residual variance of
dipfits. If you haven't yet, please be sure to walk through the online
eeglab tutorial doing the same steps with the eeglab tutorial data.
This is useful for understanding how things should look and what to
expect.

Review published articles, and poll current researchers, about what is
the usual, acceptable, and OK levels of residual variance.

You should be careful about using both PCA and ICA. My understanding
is the recommendation from eeglab is to just use ICA. This might be
impacting your ICA-results quality.

Make sure you are not trying to get "lower" residual variance of
dipole fits with "bad ICs".

Review the normal residual variance of dipfit for the ICs in the
online eeglab tutorial datasets. Compare to your expectations. Compare
to published dipfit variance results.

To get lower residual variance per IC, your IC scalp maps need to be
"better" in terms of being more bipolar, etc..

You have a low amount of time in your recording, so you may want to
downsample in channels.

I would say review your continuous pre-ICA and post-ICA data to make
sure there is not noise or artifacts that can be removed that are
being included, possibly influencing your IC quality (and thus
dipfit).

I believe that Makoto's pipeline recommends a 1hz high-pass for ICA.
Also, it's often recommended to filter at 1-50 or 1-40 hz before doing
ICA.

Probably best to test multiple settings on a few files, rather than a
few settings on many kinds of files.
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