[Eeglablist] Baselining and filtering for ICA with epoched data

Mon Aug 20 10:02:45 PDT 2018

Dear Jumana,

> The recommendation on the page is to apply 1Hz to 0.1Hz for ERPs. Is this
still the recommendation?

No. I thought I have never recommended 'ICA on 1-Hz data, then copy it to
0.1Hz data' strategy as a part of my preprocessing pipeline. This is
because not only ICA but also ASR also benefits from using a high-pass
filter at 1-2 Hz (see the lower-end of the IIR spectral weighting on
https://sccn.ucsd.edu/wiki/Makoto%27s_preprocessing_pipeline#ASR_FAQ_.28updated_03.2F19.2F2018.29).
The developer of ASR told me that I should use 0.25 Hz to 0.75 Hz as
transition band when applying high-pass filter, which is twice as sharp as
applying the '1-Hz high-pass filter' with the default EEGLAB filter
function (whose transition band is 0 to 1 Hz). An unmixing matrix resulting
from ICA can be copied from one dataset to others, but ASR result is the
time-series itself, you can't copy it to others. For this reason, I do not
use 'ICA on 1-Hz data, then copy it to 0.1Hz data' strategy myself, and (I
thought) I have never regarded as a 'necessary process as a default'. I
still think it is an interesting idea and creative attempt. Certainly there
could be situation I could benefit from using that strategy.

Another reason is that I pay more attention to results in time-frequency
domain which is usually > 3 Hz (this is from limitation of time-frequency
analysis: resolving 3 Hz already requires 1.1s-long sliding window with our
recommended parameters. If you want to resolve 1 Hz, you'll need 3.3s-long
window, which would be longer than SOAs in most of ERP paradigms). An
averaged ERP is a broadband phonemenon, broad toward the lower end. If you
want to investigate ERP in the traditional sense, the analysis deserves a
dedicated pipeline.

Makoto

On Sat, Aug 18, 2018 at 12:03 AM Ahmad, Jumana <jumana.ahmad at kcl.ac.uk>
wrote:

> Hi Makoto,
> The recommendation on the page is to apply 1Hz to 0.1Hz for ERPs. Is this
> still the recommendation?
>
> Best wishes,
> Jumana
>
> On 18 Aug 2018, at 05:29, Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:
>
> 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
> <https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsccn.ucsd.edu%2Fwiki%2FMakoto%2527s_preprocessing_pipeline%23What_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&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7Cb3d0a2cd8aa34ddf2bb908d604c33a27%7C8370cf1416f34c16b83c724071654356%7C0&sdata=%2Bz9ExPTZDZ5YiFMdg813%2FmTlBlO84WYh%2B3Ci%2FtpHBtU%3D&reserved=0>
>
> 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://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww2.bc.edu%2Felizabeth-kensinger%2F&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7Cb3d0a2cd8aa34ddf2bb908d604c33a27%7C8370cf1416f34c16b83c724071654356%7C0&sdata=KcVqxF0dYQuLGwxQrdqTKtGapXQ50JJGuZRNF77VknQ%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%7Cb3d0a2cd8aa34ddf2bb908d604c33a27%7C8370cf1416f34c16b83c724071654356%7C0&sdata=8Y0nB4ZbKmlCkeJmTdOizkJmb6qetCza11gGBhozLZ4%3D&reserved=0>,
>> 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*
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>>>
>>> ------------------------------
>>> *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%7Cb3d0a2cd8aa34ddf2bb908d604c33a27%7C8370cf1416f34c16b83c724071654356%7C0&sdata=KcVqxF0dYQuLGwxQrdqTKtGapXQ50JJGuZRNF77VknQ%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%7Cb3d0a2cd8aa34ddf2bb908d604c33a27%7C8370cf1416f34c16b83c724071654356%7C0&sdata=8Y0nB4ZbKmlCkeJmTdOizkJmb6qetCza11gGBhozLZ4%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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