[Eeglablist] Splitting the data for ICA

[Makoto Miyakoshi]

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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