[Eeglablist] Interpolating bad channels while avoiding PCA before ICA

[Jeff Eriksen]

EEGlab community:

Concerning channel interpolation, I have wondered about using non-linear spatial interpolation, specifically if one can avoid reducing rank by using it. We all know that linear interpolation is bad in this regard, but the better nonlinear interpolation methods, based on mathematical or physical theory, may provide a way around this. I am thinking of spherical splines, spherical harmonics, or 3D splines. Has anyone out there tried this? I assume most commercial and academic interpolation use linear interpolation, so one would have to make a special effort.

-Jeff Eriksen
OHSU

From: Mikołaj Magnuski <imponderabilion at gmail.com<mailto:imponderabilion at gmail.com>>
Date: Sunday, May 19, 2013 4:11 PM
To: "eeglablist at sccn.ucsd.edu<mailto:eeglablist at sccn.ucsd.edu>" <eeglablist at sccn.ucsd.edu<mailto:eeglablist at sccn.ucsd.edu>>
Subject: [Eeglablist] Interpolating bad channels while avoiding PCA before ICA

Dear EEGLab community,

Given that PCA is not recommended before ICA,
how to avoid it while still interpolating bad channels?

My initial analysis of the data is in channel space
so I would like to have all channels for all participants.
Therefore I want to interpolate bad channels (I have a
maximum of 4 bad channels out of 64 per subject).
This causes the rank of the data to decrease and compli-
cates using ICA for artifactual components removal.

Do you think a following way of avoiding PCA before ICA is ok?
1. preprocess the data before ICA
2. perform ICA and remove artifactual components
3. interpolate channels

So, in short: is it valid to interpolate channels only after
removal of artifactual ICs?

Mikolaj M
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