Hello @eduardo and welcome to the forum!
Applying a notch filter is not necessary and I personally am not very fond of them anyways … if you don’t need the “high” frequencies, you can simply apply a low-pass with a cut-off frequency well below the power-line frequency. But this is not necessary for ICA. Only the high-pass filter (e.g., 1 Hz lower bound) is important to remove slow drifts.
But which events? 
You have resting-state data, right?
You can chop the continuous data into equidistant chucks using make_fixed_length_epochs()
This sounds good in general. If the data you want to analyze requires a lower filtering cutoff for the high-pass, the recommended approach is to actually create two sets the data, one filtered with a 1-Hz-or-so high-pass for fitting ICA, and another one with a lower cutoff (e.g., 0.1 Hz), to which you will apply the ICA artifact removal.
That’s how I would do it, simply to have those cleaned epochs around in case I decide to do something else with them (source localization, machine learning) later on.
You cannot fit ICA to an evoked object in MNE (only epochs and raw data); however you can apply ICA cleaning to evoked data.