Isolating stable EEG data across all channels in EEGLAB
In this conference paper they propose an algorithm that roughly goes as follows (if you can't access it, just add "sci-hub.bz" after ".org" in the url and it redirects you directly to the pdf) :


Divide channels into four groups: 1) frontal, 2) central, 3) temporal and 4) parietal+occipital.
For each group take three signals: 1) the raw signal, 2) the alpha band(8-12Hz, using a 3rd order butterworth band-pass filter) and 3) the beta band(13-35Hz).
For each group and each signal calculate the four metrics: 1) max amplitude, 2) standard deviation, 3) kurtosis and 4) skewness. For the frequency band signals they also take the mean power and the standard deviation of the power.


They then go on and find optimal thresholds for each metric, group, and signal by using a differential evolution algorithm on training data, so unfortunately it's not immediately applicable for you. As I see it, you have at least two options here:


Create 5 seconds long epochs of your data and label a lot of them. Use that data to learn optimal thresholds (unfortunately they don't give any details on how to do that, so you would have to figure that out yourself). This would obviously take a lot of effort but at the end you can be quite confident about your classification.
Just try some value ranges for the different metrics, you can orient yourself on the values they found in the paper (you have to read them from the plots though).  Check the resulting classifications. Pick the threshold values that give the "best" results by inspections. This is not as systematic as the first option but might fit your needs already. For example if you just want to avoid to have segments that were falsely classified as "clean" you might just go for very conservative/safe threshold values. There is a big danger here though, that you will get lost in an endless loop of "maybe if I adjust this threshold a bit more" and will never stop exploring. To help with that, you might also reduce the number of metrics, groups or signals.