[Eeglablist] Re. Using ICA with interpolated channels

Robert Whelan whelanrob at gmail.com
Tue Sep 28 03:04:12 PDT 2010


Jordi Costa Faidella wrote "Is it correct to perform an ICA on a dataset in
which some of the channels have been interpolated?"

This is an interesting question and we considered both orders (each order
has some advantages and disadvantages) for the FASTER method. Ultimately, we
decided to run interpolation first followed ICA. Here was our rationale:

As the EEGLAB manual recommends – “ICA works best when given a large amount
of basically similar and mostly clean data.” (see p.59). Therefore, an ICA
on a dataset in which some channels are noisy (perhaps with a lot of
non-stereotypic data due to a problem with the electrode) may decrease the
quality of the ICA (i.e., dissimilar activations are mixed into the ICs).

On the other hand, interpolating before ICA raises a couple of issues 1) it
reduces the dimensionality of the data and 2) introduces some non-linearity
into the data (if the interpolation method was not linear), which is
detrimental to the ICA solution. We dealt with Issue 1in FASTER by
restricting the maximum number of ICs to correspond with the reduced rank of
the data after interpolation.

The choice then was between reducing the quality of the ICA by introducing
noisy channels or reducing the quality of the ICA by the non-linearity
introduced due to spherical interpolation. Although ICA assumes linearity,
there is almost certainly some non-linearity in the signals recorded at the
scalp, and the non-linearity introduced by spherical interpolation is likely
only a small contributer to the overall non-linearity. In any case, based on
pilot testing we found that when the ICA was done with noisy channels
included (i.e., not interpolated out) the resulting components were less
useful than when the data were cleaner (i.e., the channels were
interpolated). As an aside, testing algorithms on real data proved much more
informative than testing on the simulated data, perhaps due to the inclusion
of non-stereotypic artefacts in the real data.

That said, we are certainly open to persuasion on this issue and/or
suggestions about how to quantify which order is better. Also, might there
be situations in which one order is superior to the other, perhaps depending
on the maximum number of ICs that can be generated?

If there is demand, we can also configure FASTER so that the user can select
the order of the processing steps. Email me directly robert.whelan at tcd.ie or
whelanrob at gmail.com if this is something that people might want or with any
other suggestions.

Best Regards,

Rob & Hugh

-- 
Robert Whelan, PhD
Senior Research Scientist

Trinity Centre for Bioengineering
Trinity College Dublin

Department of Neurology
St. Vincent's University Hospital
Elm Park, Dublin 4

webpage: http://www.mee.tcd.ie/~neuraleng/People/Robert
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