Add SegmentationReconstruction augmentation

[gustavohenriquesr]

Hi, I created a new data augmentation class named SegmentationReconstruction to braindecode. This process was originally proposed in this paper, which I implemented and used in my own work.

This technique works on the time domain, and the ideia is to segment each trial into several segments and then generate new artificial trials, as a concatenation of segments coming from different and randomly selected training trials from the same class.

[codecov]

Codecov Report

Attention: Patch coverage is 92.72727% with 4 lines in your changes are missing coverage. Please review.

Project coverage is 87.25%. Comparing base (a9f744f) to head (6d0b492).

Additional details and impacted files

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+ Coverage   87.20%   87.25%   +0.05%     
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  Files          67       67              
  Lines        5925     5980      +55     
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+ Hits         5167     5218      +51     
- Misses        758      762       +4     

[bruAristimunha]

Hey @gustavohenriquesr,

Thank you for your first contribution!

I am super happy about this ;)

I made some initial comments and I will try to ask for some extra revision, as I am already familiar with the code.

[bruAristimunha]

Extra comment, please edit the source doc to include in the documentation page.

[cedricrommel]

Thanks a lot for the contribution @gustavohenriquesr ! It's been a while I wanted an augmentation like this to be added to the library and I'm glad you are doing it!

I think the PR is great and have just a few comments. Most of them are small things, except maybe for the location where randomness should be introduced: we made the design choice of only sampling:doing random operations outside functionals.py, to stay close to torchvision design.

[gustavohenriquesr]

@bruAristimunha

I have removed some functional tests that no longer apply with the new rearrangement of functions here 882bd61. The cases handled in the functions are now at the class level (transforms.py) and are no longer useful for functional testing. We can add more tests to the transform class, if you think it's necessary.

[cedricrommel]

LGTM!!

Thanks for the changes and explanations!

[bruAristimunha]

Thank you so much, @gustavohenriquesr! I hope this makes our script simpler with this augmentation now integrated, and I am hoping for the paper to be accepted! 🤞🏽

Thank you so much, too, for your revision, @cedricrommel; I would love to have more reviews from you here if you have time for open source 🙏🏽

[gustavohenriquesr]

Glad to contribute! I also thank you @bruAristimunha @cedricrommel for helping me improve the code. I hope to be able to make further contributions in the future!