Similarity between two signals: looking for simple measure

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I have 20 signals (time-courses) in group A and 20 signals in group B. I want to find a measure to show that group A is different from group B. For example, I ran xcorr for the signals within each group. But now I need to compare them somehow. I tried to take a maximal amplitude of each xcorr pair, which is sort a measure of maximal similarity. Then I compared all these values between two groups, but there was no difference. What else can I do? I can also compare frequency spectrum, but then I again do not know what frequency bin to take. Any suggestions / references are highly appreciated!

I have about 20 signals in each group. Those are my samples. I do not know a-prirori what might be the difference. Here I bring the 9 sample signals for each group, their auto-correlation and cross-correlation for a subset of signals (group 1 vs. group 1, group 2 vs. group 2, group 1 vs. group 2). I do not see any evident difference. I also do not understand how you propose to compare cross-correlations, what peaks should I take? All the signals were detrended and z-scored.

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signal-processing signals matlab
2 Answers

With reference to Brian's answer above, I've written a Python Function to compute the similarity of time-series signal as below;

def compute_similarity(ref_rec,input_rec,weightage=[0.33,0.33,0.33]):
    ## Time domain similarity
    ref_time = np.correlate(ref_rec,ref_rec)    
    inp_time = np.correlate(ref_rec,input_rec)
    diff_time = abs(ref_time-inp_time)

    ## Freq domain similarity
    ref_freq = np.correlate(np.fft.fft(ref_rec),np.fft.fft(ref_rec)) 
    inp_freq = np.correlate(np.fft.fft(ref_rec),np.fft.fft(input_rec))
    diff_freq = abs(ref_freq-inp_freq)

    ## Power similarity
    ref_power = np.sum(ref_rec**2)
    inp_power = np.sum(input_rec**2)
    diff_power = abs(ref_power-inp_power)

    return float(weightage[0]*diff_time+weightage[1]*diff_freq+weightage[2]*diff_power)
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