[Mne_analysis] too short epochs

S.P.H. Speer speer at rsm.nl
Mon Jun 8 09:46:20 EDT 2020
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Hi Eric,

Thanks for your response. Yes, I downsampled the raw data using the resample method. I did this because I am using pyprep for the initial stages of preprocessing and have got data from an experiment that runs for 40 minutes. So the memory requirements become huge. Is there a way to downsample the data and avoid these issue? How does the events array need to be adjusted?

Thanks for your help,


> I've been preprocessing my EEG data using standard preprocessing steps
> such as highpass filtering (1Hz), line noise removal, downsampling,
> removing noisy channels, and ICA and downsampling using MNE

Downsampling the raw data or when constricting epochs with `decim` or after
creating epochs with `epochs.decimate`? Generally downsampling / resampling
raw is discouraged...

> When I then epoch my data it drops the majority of the epochs (40 out of
> 70 with a sampling frequency of 250 and 60 out of 70 with a sampling
> frequency of 100). The drop_log indicates that all of the epochs were
> dropped because they were too short.

This can happen if you resample raw and then don't adjust your `events`
array to compensate, perhaps this is what happened?


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