The low-level read_neuralynx_ncs function detects the presence of gaps in the .ncs file 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 #11969 (comment)
It sounds like
mne.io.read_raw_neuralynx()should minimally check for temporal gaps betweenneosegments and issue a warning? For example, it should check that eachneo.Segment[i]object starts when theneo.Segment[i-1]ended and raise a warning if this is not the case (i.e. there's temporal gap, assuming the information inneois 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 #11969 (comment)