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 score method for use with GridSearchCV
  • 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 Epochs class
  • forward/backward model tutorial?
  • plot method that is smart enough to at least deal with 1D (STA) and 2D (STRF) plotting, probably using NonUniformImage class rather than pcolormesh
  • decimation (decimate data before fitting or model after fitting?)
  • refactor with linear_regression_raw, XDawn (categorical data, #4940)

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. 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

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