[Eeglablist] Baselining and filtering for ICA with epoched data

[Eric Fields]

Hi Jumana and others,

To be clear, my question isn't about implementation (I know it is
relatively easy to calculate ICA weights from one dataset and apply them to
another). My question was about how this effects the data.

If I calculate the ICA weights using data filtered at 1 Hz, by definition
this solution will *not* give maximally independent components when it is
applied to 0.1 Hz filtered data. In the 0.1 Hz filtered data there is low
frequency information in the data that ICA was not trained on and therefore
doesn't "know" anything about. That low frequency information must,
mathematically, end up somewhere in the IC activations when I multiply the
unmixing matrix with the 0.1 Hz filtered data. What are the consequences
and pitfalls of this? Is it something to worry about? Under what
circumstances?

My guess is that the low frequency information gets divided across the ICs
in a somewhat a priori unpredictable way depending on the scalp
distribution of the low frequency information and the scalp distribution of
the ICs. If I then remove one of the ICs, I may remove some of this low
frequency information with it. If so:

   1. If the low frequency information in question is purely noise, this
   could lead to some pattern of noise/artifact in the data that is hard to
   interpret or move noise to electrodes that didn't originally include it.
   2. One of the reasons for using the 0.1 Hz filter is that part of the
   effects I am interested in (e.g., later ERP components) contain information
   below 1 Hz (see Tanner et al., 2015). Can I be confident that ICA does a
   good job of isolating artifact from neural activity of interest if part of
   that activity of interest was not present in the training dataset?


Have these issues been addressed anywhere in the literature or does anyone
have recommendations?

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

On Wed, Feb 21, 2018 at 1:00 PM, Ahmad, Jumana <jumana.ahmad at kcl.ac.uk>
wrote:

> It depends if you want to examine component activity below 1Hz. Most
> artifacts of interest, such as blinks and saccades should be higher
> frequency etc.
>
>
> *------------------------------------------*
> *Jumana Ahmad*
> Post-Doctoral Research Worker in Cognitive Neuroscience
> *EU-AIMS Longitudinal European Autism Project (LEAP) & SynaG Study*
> Room M1.26 <https://maps.google.com/?q=M1.26&entry=gmail&source=g>.Department
> of Forensic and Neurodevelopmental Sciences (PO 23) | Institute of
> Psychiatry, Psychology & Neuroscience | King’s College London | 16 De
> Crespigny Park | London SE5 8AF
> <https://maps.google.com/?q=16+De+Crespigny+Park+%7C+London+SE5+8AF&entry=gmail&source=g>
>
> *Phone:* 0207 848 5359| *Email:* jumana.ahmad at kcl.ac.uk
> <antonia.sanjose at kcl.ac.uk> | *Website:* www.eu-aims.eu | *Facebook:*
> www.facebook.com/euaims
>
> ------------------------------
> *From:* eeglablist <eeglablist-bounces at sccn.ucsd.edu> on behalf of Eric
> Fields <eric.fields at bc.edu>
> *Sent:* 21 February 2018 03:45:30
> *To:* EEGLAB List
> *Subject:* [Eeglablist] Baselining and filtering for ICA with epoched data
>
> 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://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww2.bc.edu%2Felizabeth-kensinger%2F&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7C60790510144d4d17f8f608d579548d03%7C8370cf1416f34c16b83c724071654356%7C0&sdata=Vmoa8t0Q6S95V85lnDxKRWHj4Wt%2B2Pw87O2v5nSZE%2BM%3D&reserved=0>,
> Boston College
> Aging, Culture, and Cognition Laboratory
> <https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.brandeis.edu%2Fgutchess%2F&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7C60790510144d4d17f8f608d579548d03%7C8370cf1416f34c16b83c724071654356%7C0&sdata=KkZ5TQyb7BlOduU%2BXU66q2I5qJJXDvjk%2BPguBy3veQI%3D&reserved=0>,
> Brandeis University
> eric.fields at bc.edu
>
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