[Mne_analysis] Discussion: robust, linear and polynomial detrending

[Diptyajit Das]

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Thanks, Marijn. It was really helpful.

On Mon, Feb 24, 2020 at 6:02 PM Marijn van Vliet <w.m.vanvliet at gmail.com>
wrote:

>         External Email - Use Caution
>
> Dear Dip,
>
> It is not that hard to create Raw and Epochs data structures with custom
> data. You do this using the RawArray and EpochsArray classes, see here:
>
>
> https://mne.tools/stable/auto_tutorials/simulation/plot_creating_data_structures.html
>
> Note that you can just re-use the .info from the original raw and can skip
> the parts on how to create a custom Info object.
>
> Once you get comfortable creating these datastructures, you can explore
> different ways to de-trend the data as you please, with the usual
> numpy/scipy functions.
>
> best,
> Marijn.
>
>
> On 24/02/2020 10:54, Diptyajit Das wrote:
>
>         External Email - Use Caution
> Dear MNE community,
>
> I would like to discuss the following issues:
>
> Data background: Our data (MEG-EEG) is often composed with slow drifts
> (mostly due to street artifacts). We are interested in finding out cortical
> activity in lower frequency states, therefor a high pass filter option
> (>.5-1hz) is not a optimal solution for us since it removes most of the
> signals that we are interest in.
>
> Some of the highlighted points:
> 1. Current high filter setting has set to (.1 hz as lower cutoff), we can
> see the spatial map of the drift components in the ICA decomposition but
> ICA decomposition for other artifacts (eye blinks/cardiac) is not stable
> which we already expect at this point. Removing the drift components
> directly from ICA and then rerun the ICA for the second time to remove
> other biological artifacts sound suspicious to me. I am not an expert of
> ICA decomposition but as far I know, removing PCA components will alter the
> linear projection of the sources. How valid is the idea of ruining ICA for
> two times?
>
> 2. MNE offers a dc and a liner detrending option to remove show drifts at
> the epochs/evoked level. But often I have seen linear detrending removes
> activity that is coming from cortical sources  (i.e., not a optimal fit for
> our data), in fact  a lower order polynomial fit is actually more
> reasonable.  A recent work by  de Cheveigné A
> <https://www.ncbi.nlm.nih.gov/pubmed/?term=de%20Cheveign%C3%A9%20A%5BAuthor%5D&cauthor=true&cauthor_uid=29448077>
> (https://www.ncbi.nlm.nih.gov/pubmed/29448077) [method: weighted robust
> detrending] is a nice idea to tackle this kind of issue. The algorithm has
> been adapted in 'MEEGKIT' package [
> https://nbara.github.io/python-meegkit/auto_examples/example_detrend.html#sphx-glr-auto-examples-example-detrend-py]
> but lately I have figured it out that it still needs more testing. Now,
> coming back to my question: Is there any way to implement such algorithm
> directly at the raw data? So far I am not able to modified the raw mne
> structure with synthetic values (after removing the trend with a polynomial
> fit) while keeping the same info directory. Any advice or suggestion would
> be helpful at this stage.
>
> 3. Is there any way to apply a linear detrending only to a sub set of the
> data (either meg/eeg even though they are combined at epochs level) ?, so
> far its implemented in evoked level as I have seen. It would be ideal that
> if I can somehow apply the linear detrending only to a subset of the data
> at the epochs level (i.e., meg in my case) instead of doing it at evoked
> level. I am willing to contribute in this case.
>
>
> Best,
> Dip
>
>
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