[ENH] Add PSD feature via MNE

[anandsaini024]

PSD feature creation script.

[adam2392]

Just to clarify, how are these and the topographic PR different from what is included in ICLabel?

I might've missed this discussion.

[codecov[bot]]

Codecov Report

Merging #73 (07888b3) into main (33856df) will increase coverage by 0.08%. The diff coverage is n/a.

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@@            Coverage Diff             @@
##             main      #73      +/-   ##
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+ Coverage   86.76%   86.84%   +0.08%     
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  Files          15       16       +1     
  Lines         831      859      +28     
  Branches      101      105       +4     
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+ Hits          721      746      +25     
- Misses         82       83       +1     
- Partials       28       30       +2     

Impacted Files	Coverage Δ
main/mne_icalabel/utils/_docs.py	93.10% <0.00%> (ø)
main/mne_icalabel/features/psd.py	89.28% <0.00%> (ø)

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[mscheltienne]

@adam2392 The ICLabel ones are a copy/paste of the MATLAB features.. which are not super clean and nice. For instance, the ICLabel rpsd feature is supposed to have a random component right? Well.. in practice that's not the case because it does something along the lines of:

some_2d_matrix = [..., ...]
subsets = [indices of all columns]  # instead of an actual subset
select_and_shuffle = shuffle(some_2d_matrix [:, subsets])  # shuffle the columns
average = avg(select_and_shuffle, axis=1)  # avg across columns

And that's not the only example of 'weird' coding choices/behaviors. I added comment in the features.py file describing those items when I ran into them, e.g. https://github.com/mne-tools/mne-icalabel/blob/26a2f1fb2829d7f3ee7019c6591775be33ecde98/mne_icalabel/iclabel/features.py#L375-L378

which corresponds to the pct_data variable in this file https://github.com/sccn/ICLabel/blob/d0cfb0f8638cd4518ee2e66a3bff0bb32ff40969/eeg_autocorr_welch.m#L1-L25

variable which is never provided to the function https://github.com/sccn/ICLabel/blob/d0cfb0f8638cd4518ee2e66a3bff0bb32ff40969/ICL_feature_extractor.m#L77

Or this one: https://github.com/mne-tools/mne-icalabel/blob/26a2f1fb2829d7f3ee7019c6591775be33ecde98/mne_icalabel/iclabel/features.py#L426-L429

which corresponds to https://github.com/sccn/ICLabel/blob/d0cfb0f8638cd4518ee2e66a3bff0bb32ff40969/eeg_autocorr_welch.m#L36-L44

---

Overall, I'm not super confident in the ICLabel features, so I asked Anand to prepare functions to extract features from Instance/ICA decomposition using MNE, starting with the topographic map and the PSD based on the ica.plot_properties method. The idea is to use those functions to extract features for future models.

[adam2392]

FYI to fix the style easily and the sorting of imports:

black ./mne_icalabel/* and isort ./mne_icalabel commands, or type make black and make isort. The other checks like flake8 (style), pydocstyle, and mypy can be fixed manually.

[mscheltienne]

@anandsaini024 Please look into additional tests as we discussed:

• test inspired from tests of the PSD functions in MNE
• test on signals with known frequency content, e.g. sinusoid, amplitude modulated signal, ...
• test with different inputs for the arguments of get_psds

I'll try to double-check this PR this week.

[mmagnuski]

Hi, I popped in to see whats new in mne-icalabel (great project!) and saw your discussion of pct_data variable and how it is not used in the Matlab code. I think (but I may be wrong) that it is related to how EEGLAB usually computed PSD - to save time the spectrum was computed only for a portion of the data (especially important for very long raw recordings and the fact to you expect instant feedback when working via GUI). This option was controlled with a special parameter that in research usage would be set to 100. I think this may be the reason for the behavior of pct_data you see.

[mscheltienne]

Maybe, I didn't dig in too far, but it looked to me like the feature extraction code I translated was half-baked. And I think the unused pct_data variable appears both in the PSD and autocorrelation features.

Also, I'll finalize this PR at some point ;)

[mscheltienne]

This is now too out of date, especially with the added Spectrum objects. Closing.