mne-tools / mne-tools/mne-python
Three level mixed norm inverse solutions
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ENH
HARD
- Dominant language
- Python
- Stars
- 3.5k
- Forks
- 1.6k
- Avg merge
- 1d 6h
- Merged PRs (30d)
- 100
Description
Is mixed norm L212 source localisation i.e. optimising for spatially distinct, sparse solutions across multiple experimental conditions (described in Gramfort et al, 2012) implemented in MNE yet?
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
No files, tests, or entry points are named. Search the repository for mixed-norm, L21, and Gramfort-related source-localisation code, then check existing documentation and tests for support across multiple experimental conditions. Done would require a clear implementation or a documented answer about whether this method is supported.
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
- Needs clarification
- Newbie friendliness
- 18/100