Firstly, thank you for this great software. I have a question about combining multiple datasets/trials from the same window/field-of-view. As there might be some delay and changes between trials, one cannot always assume that the properties of signals remains identical (i.e., intensity and noise levels). Assuming that all trials are spatially aligned, I want to have the same spatial component over all trials, while each trial has a unique temporal component. This is typical "multi-view" problem and I see basically two ways to solve it: (1) temporally concatenate data into one large matrix or (2) keep trials separate and compute an optimal (consensus) spatial component over all trials. The first approach is simple, but poses a problem if signal properties vary (should you normalize data before concatenating? What about the noise level?). Also the size of matrix could become huge with lots of trials. For the second approach one would need to modify the code/algorithm for spatial initialization and component updating. Could one simply combine individual solutions by (weighted) averaging or taking median? One could also give each trial some weight (i.e., number of samples) that would affect the averaging procedure. Updating of temporal components would remain as is. What is your recommended strategy in dealing with multi-trial data?