Comparing similarity of two simultaneously recorded EEGs in freq domain I have two EEG signals that are simultaneously recorded during the night (sleep). Imagine one device recorded from channel F4 and the other from Fp2. Now I would like to check the similarity of these signals, so, the first step is to sync them which has been done successfully using cross-correlation :)
I can also report some similarity metrics in time-domain, e.g. Pearson correlation per window (e.g. each 30 s) to quantify the similarity of signals.
The problem arises when I want to compare (or let's say quantify) them in the freq domain. When I plot e.g. spectrogram or PSD we can see their similarity visually; however, I don't know how to quantify this similarity in the frequency domain. By vision the spectrograms (or PSDs) are quite similar but I guess it is not a powerful reason without reporting any similarity metric as a number. Is there any metric by which I can report similarity of PSDs or spectrograms window-by-window and based on each frequency bin (Delta, Theta, etc)?
You can express correlation in the frequency domain but the closest thing to what you are describing would be Coherence.
It returns a number between 0 and 1 per each frequency bin of the spectrum. The closest this number is to 1 the more &quot;similar&quot; the signals would be and you can average this number over the whole spectrum or over a frequency range.
Hope this helps.