[Eeglablist] Baselining and filtering for ICA with epoched data

Fri Aug 17 21:29:05 PDT 2018

Dear Eric and Jumana,

I summarized my explanation to this wiki page. Sorry I did not notice I
made so many typos...
https://sccn.ucsd.edu/wiki/Makoto%27s_preprocessing_pipeline#What_happens_to_the_.3C_1_Hz_data_if_ICA_is_calculated_on_.3E_1_Hz_data_and_applied_to_0.1_Hz_data.3F_.2808.2F17.2F2018_Updated.29

Makoto

On Tue, Feb 27, 2018 at 9:14 AM Eric Fields <eric.fields at bc.edu> wrote:

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