[Eeglablist] Splitting the data for ICA

[Scott Makeig]

Jan -

I do have a new thought you might try ...
1 . ICA decompose the highpass data (say >1 Hz).
2. remove the back-projections of well captured brain and non-brain (e.g.,
eye movement) ICs
This will leave a full-channel count but lower-rank dataset.
3. Lowpass filter this dataset.
4. ICA decompose these data, first retaining by PCA, #dims = #channels -
#removed_ICs

This will not isolate sources into single ICs that result from moving
source excitations, but might reveal some useful structure in the lowpass
data.

Scott



On Mon, Sep 4, 2023 at 10:22 AM Jan Karsten via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Dear Scott and Makoto,
>
> Thank you for your answers, they give me some ideas to test out and
> discuss with my colleagues (& share them, if meaningful). But as you said,
> I guess only a quantitative evaluation can give an answer regarding best
> practices.
>
> Running the ICA on the bandpass filtered data (0.05 - 5 Hz) does not give
> any helpful results (even with a very low rank) for my data; the
> decomposition was not able to adequately isolate obvious artefacts
> (eyeblinks, cable artefacts...).
>
> Regards,
> Jan
>
> -----Ursprüngliche Nachricht-----
> Von: eeglablist <eeglablist-bounces at sccn.ucsd.edu> Im Auftrag von Makoto
> Miyakoshi via eeglablist
> Gesendet: 29 August 2023 23:32
> An: eeglablist at sccn.ucsd.edu
> Betreff: Re: [Eeglablist] Splitting the data for ICA
>
> Dear Jan,
>
> > Do you think that the low-frequency data within the ICA sources is
> problematic or can it be ignored? What are your suggestions on how to best
> deal with such data?
>
> Here, you are asking a qualitative question. Often, this kind of question
> is answered by seeing quantitative evaluation.
>
> > I am concerned, that this low frequency content will be randomly
> distributed across the ICA components when applied to the second dataset
> (in my case with a band pass filter of 0.05 - 5 Hz).
>
> It is NOT random.
> You can think of the problem as follows.
> Let's say you apply ICA 2 Hz, then copy the ICA weight matrix to the
> broadband data that contains 1-2 Hz signals.
> What you can do is that you band-pass filter the data to 1-2 Hz first.
> This tells you the exact data ICA missed. You can study their
> non-stationary scalp topography changes etc.
> Now, you apply the > 2Hz ICA to this 1-2 Hz band-pass filtered data. This
> tells you what will be added when you apply > 2 Hz ICA to your broadband
> data.
>
> If you like, you can also run ICA on your 1-2 Hz data (I recommend you
> specify very low rank decomposition because such as narrow band-pass
> filtered data could be severely rank reduced; see my ICA-rank paper from
> the link below. The obtained ICA may show more correlation to your
> broadband ICA results rather than > 2Hz ICA results, IF the 1-2 Hz signal
> has dominant power over the entire freq spectrum (there is 1/f PSD curve,
> so lower the frequency, higher the power in general).
>
>
> https://urldefense.com/v3/__https://www.frontiersin.org/articles/10.3389/frsip.2023.1064138/full__;!!Mih3wA!FTktodiwtkv_Agi2VLBbfwOviUhXg5O8pVlMiA1I0MAQxJqds0JJt441KG9uPc-s2qpW3JM4wLxchFs0UArw40XAVX8$
>
> To conclude, your question can be answered by looking at data processed
> with a band-pass filter and ICA. After seeing these results, you can made a
> decision. Maybe the results are very obvious to you to see its meaning,
> maybe not.
> See also this Wiki article.
>
>
> https://sccn.ucsd.edu/wiki/Makoto's_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_.2805.2F18.2F2022_Updated.29
>
> Makoto
>
>
> On Tue, Aug 29, 2023 at 12:07 PM Jan Karsten via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
> > Dear list members,
> >
> > I am currently working on a preprocessing procedure for EEG data with
> > the aim to analyse the movement-cortical potential. My main purpose
> > for the preprocessing  is to reduce the amount of artefact
> > contamination (especially eye blinks) using ICA. Since we are
> > interested in the MRCP, I am following the suggestion to split the
> > data before the ICA to apply a high-pass filter (1 Hz) to the data I
> > am running the ICA algorithm on and attach the solution to the second
> dataset I am using for the analysis.
> >
> >
> > Now my question is:
> >
> > Because the ICA is trained with data that does not contain low
> > frequency content (due to the high pass filter at 1 Hz), I am
> > concerned, that this low frequency content will be randomly
> > distributed across the ICA components when applied to the second
> > dataset (in my case with a band pass filter of 0.05 - 5 Hz). Hence, in
> > the worst case, an ICA component that contains mostly eye blinks will
> > also contains more valuable information than usual in the low
> > frequency domain. Do you think that the low-frequency data within the
> > ICA sources is problematic or can it be ignored? What are your
> suggestions on how to best deal with such data?
> >
> >
> >
> > I attached a picture to the mail for clarification (I hope it can be
> > seen through the list), showing an ICA component containing eye blinks
> > acquired from the split datasets.
> >
> > Thanks in advance for your help and I am looking forward for your
> > replies,
> >
> > Jan
> > _______________________________________________
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-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University of
California San Diego, La Jolla CA 92093-0559, http://sccn.ucsd.edu/~scott