I am working with EEG and now I am trying to compare coherence for two groups of individuals. Problem is coherence is dependent on length of signal but I have signals with different length for each individual. So I decided to divide my signals on equal pieces and randomly choose fixed number of pieces for each individual. It's working fine, but I'm not happy about loss of data. Then I realised that I can repeat this choosing fixed number of pieces and calculating coherence many times for each individual. It gives me empirical distribution of coherence for each individual. So my qvestion is: How to compare two groups of empirical distributions where each distribution come from one individual?
I found related question. Is this method appropriate in my case? I tried to normalize my data as in accepted answer on this question with log, arctan, sqrt transform. But with no success.