[Eeglablist] Baselining and filtering for ICA with epoched data

[Eric Fields]

Hi,

I know there have been other threads related to this, so I apologize if
this has been addressed directly and I missed it.

Groppe et al. (2009) showed that ICA gives more reliable results if you use
the full epoch instead of the prestimulus period to baseline. The reason
generally given for this is that baseline correction changes the scalp
distribution of sources depending on what is happening in the baseline
period. By this logic, using the full epoch should improve ICA (because
longer periods are less affected by random variations), but no baseline
correction at all should be even better.

Meanwhile, Winkler et al. (2015) have suggested that ICA works best on data
high pass filtered at 1-2 Hz.

Assuming I prefer to use a 0.1 Hz high pass filter (because of distortions
1 Hz filters can cause in the ERP: Tanner et al., 2015), I have two
questions:


   1. Does the removal of additional low frequency noise you get from using
   a full epoch baseline (vs no baseline) outweigh the downsides of baseline
   correction for ICA?
   2. Alternatively, is it appropriate to apply a 1 or 2 Hz filter to the
   data used for ICA training, and then apply the ICA solution to an EEGset
   filtered at 0.1 Hz? Winkler et al. suggest this, but what happens to the
   low frequency information in the data when the ICA solution that has been
   learned without it is applied? Can this cause problems?


Thanks!

Eric

-----
Eric Fields, Ph.D.
Postdoctoral Fellow
Cognitive and Affective Neuroscience Laboratory
<https://www2.bc.edu/elizabeth-kensinger/>, Boston College
Aging, Culture, and Cognition Laboratory <http://www.brandeis.edu/gutchess/>,
Brandeis University
eric.fields at bc.edu
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