mne-tools / mne-tools/mne-python
ENH: More features for ReceptiveField
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- Dominant language
- Python
- Stars
- 3.5k
- Forks
- 1.6k
- Avg merge
- 1d 6h
- Merged PRs (30d)
- 100
Description
- support
scoremethod for use withGridSearchCV - use custom Ridge solver for speed and multiple regularization types (i.e., Ross's Fourier-based code)
- change to positive lags for causal behavior #4550
- backward transform
- Add CUDA support for the fitting
- if necessary, make tmin/tmax/sfreq logic consistent with
Epochsclass - forward/backward model tutorial?
-
plotmethod that is smart enough to at least deal with 1D (STA) and 2D (STRF) plotting, probably usingNonUniformImageclass rather thanpcolormesh - decimation (decimate data before fitting or model after fitting?)
- refactor with
linear_regression_raw,XDawn(categorical data, #4940)
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 at the existing ReceptiveField implementation and review the unchecked items against Epochs, linear_regression_raw, XDawn, GridSearchCV, and the plotting requirements. The issue contains several independent possibilities rather than one defined task, so a contributor would need to narrow the scope and agree on a concrete completion criterion first.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- data-visualization, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100