Hello,
I am very new to MNE python and EEG data analysis, and I hoped someone could help me with a bit of my preprocessing pipeline that I cannot figure out how to do. I am trying to replicate the analysis used in a paper that mentions: “After first epoching the data from -1000 to 2000 ms relative to target onset, and performing a baseline correction using a 200 ms pre-target baseline interval, we rejected data that exceeded a peak-to-peak threshold of 70 μV (in 100 ms steps)”. So, I have figured out that I could make my epochs with something like the bit of code below. But I am missing the “in 100ms steps” here, and the peak to peak rejection is applied to the whole duration of the epochs, rejecting almost all of them. I see there is a rejection_tmin and rejection_tmax argument that could be useful here, but I do not see how to use it in a way to make a 100ms step.
tmin = -1
tmax = 2
baseline = (-0.2, 0)
reject = dict(eeg=70e-6)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, baseline=baseline, reject=reject)
Best regards,
r.lamarque
- MNE version: 1.4.2
- operating system: Windows 11