[Eeglablist] Baselining and filtering for ICA with epoched data

[Makoto Miyakoshi]

Dear Eric,

> 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?

The answer is no. So-called baseline correction does not remove 'low
frequency noise'. It just removes DC (like moving up or down with an
elevator). After applying an appropriate high-pass filter, baseline
correction  has no merit for ICA. Hence, the correct way to apply ICA is to
first perform an appropriate high-pass filter, epoch the data if you want
BUT NO BASELINE CORRECTION, then ICA.

> 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,

If your purpose is to obtain good decomposition in ICA, yes this is
reasonable.

> 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?

What happens is that if the removed < 2Hz activity is temporally correlated
with >2Hz activities, they will be decomposed altogether with no conflict.
This is probably the case in most cases. However, if the < 2Hz activities
are independent of > 2Hz (such as external artifact), then they will not be
decomposed and would appear in the ICA results in an unexpected way. How
unexpectedly? It often takes a form of 'it's everywhere' but the
distribution is naturally based on where it is from, but never captured by
a single component.

Continued.

Makoto

On Wed, Feb 21, 2018 at 9:57 AM Eric Fields <eric.fields at bc.edu> wrote:

> 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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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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