mne-tools / mne-tools/mne-python
Subspace pursuit sparse source localisation
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- Dominant language
- Python
- Stars
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Description
Describe the new feature or enhancement
Hi, a while ago I implemented the the subspace pursuit algorithm for sparse source localisation described here:
https://www.sciencedirect.com/science/article/abs/pii/S1053811913009440
I wonder if there is interest in adding it to the already existing suite of tools for sparse source localisation?
Describe your proposed implementation
The branch is found here, if memory serves me correctly I followed the paper pretty closely:
https://github.com/jshanna100/mne-python/tree/greedy
Describe possible alternatives
The authors of the paper do state that their code is available on request, but I never had any luck getting it from them, which is why I did my own implementation.
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 reading the linked Subspace Pursuit paper and reviewing the proposed implementation on the greedy branch. Compare it with MNE-Python’s existing sparse source localisation tools, then clarify with maintainers which integration point and validation criteria are expected before proceeding.
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
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100