[Eeglablist] ICA filtering and IC semi automatic classification

[Tarik S Bel-Bahar]

Hi Alexandre, not sure if this has been explored or tested in a systematic
manner, you might have an opportunity for a methods article that would
benefit the ICA and EEg communities. Ideally one would show comparisons of
results across various combinations of the methods you referred to.

It is not clear if you considered the following (though it's validity needs
to be checked). The fundamental question is after dropping ICs is your data
cleaner as expected or not?
1. Run ICA on 1hz data
2. determine ICs to drop
3. apply ICA to 0.1 hz data
4. remove ICs that were determined in step 2

You may also benefit from directly contacting the lead authors for SASICA
and ADJUST for their opinion.

When you do achieve a sane solution, thanks for sharing it with the list so
that other users can benefit from your experiences.




On Wed, Apr 11, 2018 at 2:07 AM, Alexandre Obert <obert.alexandre at gmail.com>
wrote:

> Hi all,
>
> I read somewhere
> <https://github.com/CSC-UW/csc-eeg-tools/wiki/Filtering-and-ICA>that one
> could perform ICA onto data filtered with 1Hz (high-pass) and then apply
> ICA matrix onto 0.1Hz filtered data.
> However, I wonder how to deal with ICs classification algorithms such as
> SASICA or ADJUST in such process?
>
> SASICA onto 1Hz filtered data send different results from SASICA onto
> 0.1Hz filtered one.
> So, what's the best process:
> 1) performing SASICA onto 1Hz data and then reject artifactual ICs in
> 0.1Hz (don't feel comfortable with this)
> 2) forgetting such process and just using ICA and SASICA onto 0.1Hz data
> directly
> 3) following the mentioned process and performing SASICA onto 0.1Hz data
> with ICA matrix from 1Hz data
> 4) something else
>
> Alex
>
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