Hi guys,
I have an EEG data set with sampling frequencies of 160 Hz and 128 Hz. I’m trying to upsample the EEG signals to 200 Hz, so I was looking for some tips and tricks on how to properly upsample with mne to avoid imaging artifacts (distortion during upsampling). However, I couldn’t find any info on the tutorial for filtering and resampling data.
I would be very happy to learn how to design a low-pass filter (if necessary) following upsampling to avoid distortion.
I have never upsampled EEG data, and the usual re-sampling method you would come across is downsampling.
I would strongly recommend you consider downsampling your two EEG datasets to the same frequency (if having them on the same sampling frequency is your intent). The “loss” from downsampling is potentially easier to deal with than whatever you are introducing though upsampling.
Thank you for your kind reply.
The BCI community seems to have a tendency to downsample the recordings instead of upsampling, probably for good reasons. I will go on with downsampling methods as you recommend.
However, I would still like to know how to avoid (or minimize) the artifacts introduced by upsampling, just in case I might encounter a requirement that my project, say, a classification model, must meet a certain input value of sampling frequency higher than the sampling frequency of my training EEG data. I know it sounds vague. It is more of a curiosity than a necessity. It would be cool to have specific tools for this in mne.