[Eeglablist] Fwd: Baselining and filtering for ICA with epoched data

Sat Aug 18 16:13:58 PDT 2018

Hi Makoto,
Thank you so much for your helpful response!  I would like to share your
email to the email list so others can see our conversations.

Best,
Wanze
---------- Forwarded message ---------
From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
Date: 2018年8月18日周六 上午12:27
Subject: Re: [Eeglablist] Baselining and filtering for ICA with epoched data
To: Xie Wanze <xiew1202 at gmail.com>

I summarized my reply to my wiki page, hoping it is useful for another Eric
in the future!

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

Thank you again for your interest Wanze.

Makoto

On Fri, Aug 17, 2018 at 8:54 PM Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
wrote:

> Dear Wanze,
>
> Thank you for writing me. Hopefully my response to Eric was clear.
>
> > I tend to agree with him that this is because there is no trained
> weights and sphere info for low-frequency activity, and thus in this
> following function we don't know which component(s) the low-frequency
> activity will "be assigned to": data
>
> Although the concept and calculation of ICA is complicated, the product
> and how to use it is damn simple. It's just a spatial filter. Remember my
> analogy of the buffet table with four big plates. Your recognition of 'oh,
> there are four big plates' is the spatial filter. If mushroom soup is
> spilled over, you take the mushroom soup as well when you take salad,
> steak, salmon,... how much mushroom soup you take depends on on which plate
> the mushroom soup is spilled more.
>
> Makoto
>
> On Thu, Aug 16, 2018 at 8:37 AM Xie Wanze <xiew1202 at gmail.com> wrote:
>
>> Hi Makoto,
>> Do you mind sharing your opinions on Eric's concerns in his following
>> email?  Basically, as his email explains, he was uncertain whether applying
>> the ica outputs (ica weights, sphere, and icawinv) from 1 or 2Hz highpass
>> filtered data to 0.1Hz data would affect the low-frequency activity.
>>
>> My understanding is that this issue really depends on the purpose of
>> using ICA, i.e., just removing artifacts or also extracting spatial
>> distribution of low-frequency brain activity from the data, as the later
>> purpose, in my opinion, is likely to be affected by applying 2Hz ica
>> outputs (weights, sphere, and icawinv) to 0.1Hz data.  I tend to agree with
>> him that this is because there is no trained weights and sphere info for
>> low-frequency activity, and thus in this following function we don't know
>> which component(s) the low-frequency activity will "be assigned to": data =
>> (EEG.icaweights(opt.component,:)*EEG.icasphere)*data(EEG.icachansind,:).
>> Thus, if we do care about the scalp distribution and want to look at the
>> source localization of these low-frequency activities we should not apply
>> 2Hz highpassed data for ICA, right?
>>
>> However, this may not be an issue if we use these 2hz weights and sphere
>> info to remove artificial components whose scalp distribution is likely to
>> be dissimilar with the artificial components.  I also think the
>> low-frequency activity will be preserved in the channel-level data after
>> the component projection (to channels) is calculated with the following
>> code: compproj = EEG.icawinv(:, component_keep)*eeg_getdatact(EEG,
>> 'component', component_keep, 'reshape', '2d').
>>
>> Does my understanding sounds correct to you?
>>
>> Thank you very much!
>>
>>
>> *Wanze Xie, PhD *Postdoc Fellow
>> Laboratories of Cognitive Neuroscience, Boston Children's Hospital
>> Harvard Medical School
>> 1 Autumn Street, 5th Floor
>> Boston, MA 02215
>> (857) 218-5214 (Office)
>>
>>
>>
>> ---------- Forwarded message ----------
>> From: Eric Fields <eric.fields at bc.edu>
>> Date: 2018-02-21 19:01 GMT-05:00
>> Subject: Re: [Eeglablist] Baselining and filtering for ICA with epoched
>> data
>> To: "Ahmad, Jumana" <jumana.ahmad at kcl.ac.uk>
>> Cc: EEGLAB List <eeglablist at sccn.ucsd.edu>
>>
>>
>> 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
>

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