Is it correct to subtract two signals acquired over different times and trials to remove the common signal in them?
Will averaging S1 over these 60 acquisitions and S2 over 4 (Figure 1) and then subtracting the averaged signals (S2_avg - S1_avg) give me a better representation of response to Stimulus B or should both the signals be averaged across equal acquisitions (Figure 2).


Ideally, both signals should be averaged over the same amount of repetitions and the ideal here would be the biggest of the two (60). This is for each individual point estimate to "suffer" from approximately the same (and as low as possible) variance.

Potentially, you might gain some more insight if you were to use bootstraping, where you would be forming point estimates of each time instant of your time series and then examine these smaller subsets (potentially through a boxplot) to try and "guess" what is the most likely time series to be formed, give these two datasets. But the fact that averaging is suggested in the first place anyway, means that an assumption is already made for the time instance differences to be coming from a gaussian distribution.