mne-tools / mne-tools/mne-python
Wavelet-ICA implementation?
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- Dominant language
- Python
- Stars
- 3.5k
- Forks
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- Avg merge
- 1d 6h
- Merged PRs (30d)
- 100
Description
Describe the new feature or enhancement
Wavelet-ICA (wICA) as described in:
N.P. Castellanos, and V.A. Makarov (2006). 'Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis' J. Neurosci. Methods, 158, 300--312.
Describe your proposed implementation
I imagine it could be included in mne.preprocessing, similar to ICA. I don't know how different it is from regular ICA for it to make sense to be a separate method or rather to be selected from ICA via parameters.
Describe possible alternatives
Instead of a separate class, it could be implemented as a new wavelet boolean parameter of the ICA class. I believe a separate method is clearer, but maybe the process of wICA with respect to regular ICA makes it easy to implement in the aforementioned way.
Additional context
No response
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the existing ICA entry point in mne.preprocessing and the cited wavelet-ICA paper. Determine whether the proposal is best represented as a separate method or an option on ICA, then clarify the expected behavior and validation criteria with the issue discussion before implementation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 32/100