Participants performed five, 30-second blocks of a task. I plan to analyze these data using a GLM approach with a Gaussian basis function with parameters 1.0 1.0 and a tRange of -2.0 30.

My understanding is that I would receive 32 beta values per channel per participant (as well as group averaged beta values for each channel).

First, I am just looking to confirm that what is visualized in Homer after running a GLM are the model-predicted values.

Second, I have seen people take the model predicted values and, for example, collapse these across the task block for each channel to get a single number/channel that is interpreted to represent underlying cortical activity. I have also seen people take a similar approach but using the beta values as opposed to (what I assume are) the model-predicted values.

I know that these represent slightly different things, though I suspect that the final values would correlate quite well.

Are there advantages to choosing one approach over the other?