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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,
Sebastian
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?
Eric
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