mne-tools / mne-tools/mne-python
Incorporate Moving Window Average for mne.time_frequency.EpochsTFR
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Description
Describe the new feature or enhancement
I wish MNE to incorporate a method of taking a moving window average of spectral power wrt time.
A methodology of choice, used by multiple researchers, goes roughly:
- Sample frequencies either linearly or logarithmically at finite intervals between a range.
- Add a buffer on both sides of the data to prevent edge effects.
- Use the Morlet wavelet transform to find spectral power as a function of time.
- Down-sample power values by taking a moving average across small-time windows and slide the window at a particular rate.
Current MNE functions allow (1), (2), and (3) but not (4).
Describe your proposed implementation
This averaging method can be added to the mne.time_frequency.EpochsTFR class.
Describe possible alternatives
None.
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 at the mne.time_frequency.EpochsTFR class and read its existing time-frequency and spectral-power methods. Define how frequency sampling, edge buffers, moving-window size, and slide rate should interact; done means EpochsTFR supports the requested moving average and down-samples power over time as described.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100