[Eeglablist] Baselining and filtering for ICA with epoched data

Mon Aug 20 11:55:37 PDT 2018

Hi all,

I looked into this a bit with a sort of simulation. I ran ICA on a dataset
and then I artificially added a big linear drift to one channel in one
epoch.

Here is the original data:

[image: Original.jpg]

And here is the big linear drift added to AF4 in the third epoch:

[image: B4_ICA.jpg]

I then added the ICA weights (which had been calculated without this drift
artifact). Here is what the ICA component activations look like:

[image: IC_activations.jpg]

As you can see, the drift ends up in several ICs.

Here's what it looks like if I remove IC 7:

[image: Remove7.jpg]

You can see that there is now drift on Fp1, which didn't appear in the
pre-ICA corrected data. Removing other ICs affected by the drift does
something similar, but the effects of each differs a bit.

Of course, any time you have a idiosyncratic artifact in a single epoch,
something like this will happen because the ICA solution will not account
well for such an artifact. Such epochs just have to be thrown out.

But, imagine there were systematic low frequency information throughout the
study—either noise (e.g., skin potentials) or low frequency signal of
interest (e.g., part of an ERP slow wave that appears on every trial), and
this low frequency information is not part of the data you run ICA on. The
example above suggests this will get distributed across different ICs,
including perhaps ICs of interest (if you look above, it looks like part of
the drift ends up on IC 1, which is a blink component). And then if you
remove one of those ICs, you might remove part of that low frequency
information and/or change it's scalp distribution, either of which could
lead to wrong conclusions.

I don't claim to be an expert here, but I remain skeptical that filtering
at 1-2 Hz for running ICA and then applying that solution to an 0.01 or 0.1
Hz filtered dataset is a good idea. I've been running ICA on 0.1 Hz
filtered data, which is the same thing I use for ERP analysis. Outside of
really noisy/drifty datasets, that generally works fine, at least for
removing blinks and ocular artifact. Of course, if you are working the
frequency domain and you aren't looking at low frequency bands anyway, then
you can just use a more stringent high pass filter at all processing steps
and you don't have to worry about any of this.

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 Mon, Aug 20, 2018 at 1:03 PM Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
wrote:

> 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*
>>>> Room M1.26
>>>> <https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmaps.google.com%2F%3Fq%3DM1.26%26entry%3Dgmail%26source%3Dg&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7Cb3d0a2cd8aa34ddf2bb908d604c33a27%7C8370cf1416f34c16b83c724071654356%7C0&sdata=YIpZKal%2Fq0hd28zXexc4Vw6bwg3KhqJAdDXRFfCEzVw%3D&reserved=0>.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://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmaps.google.com%2F%3Fq%3D16%2BDe%2BCrespigny%2BPark%2B%257C%2BLondon%2BSE5%2B8AF%26entry%3Dgmail%26source%3Dg&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7Cb3d0a2cd8aa34ddf2bb908d604c33a27%7C8370cf1416f34c16b83c724071654356%7C0&sdata=9gcRX30Rvs8L28pGcwcGmvaL76ElDn3IhPPm9YqkJNA%3D&reserved=0>
>>>>
>>>> *Phone:* 0207 848 5359| *Email:* jumana.ahmad at kcl.ac.uk
>>>> <antonia.sanjose at kcl.ac.uk> | *Website:* www.eu-aims.eu
>>>> <https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.eu-aims.eu%2F&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7Cb3d0a2cd8aa34ddf2bb908d604c33a27%7C8370cf1416f34c16b83c724071654356%7C0&sdata=tuKnmrbWlAu0CtUbJzWKog28gt5oVyD5aJzqoXU2tnI%3D&reserved=0>
>>>>  | *Facebook:* www.facebook.com/euaims
>>>> <https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.facebook.com%2Feuaims&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7Cb3d0a2cd8aa34ddf2bb908d604c33a27%7C8370cf1416f34c16b83c724071654356%7C0&sdata=1QfxJOVjVawGksa0CBF9xezf5%2B7zxpfksQ93jeZ9kGY%3D&reserved=0>
>>>>
>>>> ------------------------------
>>>> *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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