mne-tools / mne-tools/mne-python
BUG: Gaps in neuralynx not handled properly
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Description
The low-level
read_neuralynx_ncsfunction detects the presence of gaps in the.ncsfile and issues a warning.
It sounds like mne.io.read_raw_neuralynx() should minimally check for temporal gaps between neo segments and issue a warning? For example, it should check that each neo.Segment[i] object starts when the neo.Segment[i-1] ended and raise a warning if this is not the case (i.e. there's temporal gap, assuming the information in neo is accurate)? And potentially also reconstruct/fill/mark missing samples such that the time axis (i.e. raw.times) is continuous and valid.
If this is on track, happy to open a separate issue and work on this.
Originally posted by @KristijanArmeni in https://github.com/mne-tools/mne-python/issues/11969#issuecomment-1832082578
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Research direction
Start with read_neuralynx_ncs and mne.io.read_raw_neuralynx, then inspect how neo Segment timing is represented. Done should include detecting temporal gaps between consecutive segments and issuing a warning; whether missing samples should be reconstructed, filled, or marked remains to be decided.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100