mne-tools / mne-tools/mne-python
Within-subject confidence intervals
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- Dominant language
- Python
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Description
E.g., for plot_compare_evokeds: we could have inferential CIs, i.e., so that non overlap of CIs indicates significance.
Ideally with correction for multiple contrasts or something ...
See:
https://www.mattcraddock.com/blog/2016/11/28/erp-visualization-within-subject-confidence-intervals/
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 plot_compare_evokeds entry point and read the linked within-subject confidence-interval article alongside the issue discussion. Clarify which inferential interval method and multiple-contrast correction are required; the work is done when those requirements are specified and the resulting visualization behavior is supported.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-visualization
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100