Dear all,
I have a question about multi-session data and how I can concatenate them.
I am collecting data from many sessions (or runs depending on how you want to call them) per subject. Normally, the design matrix would include conditions for each session in separate columns. However, there are some cases where some specific experimental conditions of interest occur only few times (or does not occur at all) within one session. This causes problems in estimation of the regressors. So, I would like to concatenate data over all sessions and run one big model so that each conditions of interest are well represented in the design matrix. The problem with this approach, however, is that there may be spill over effects between sessions (especially when using a highpass filter and AR(1) model). What's the best solution to this problem?
I was thinking of using the ordinary model matrix but just using the constant only (actually number of sessions -1) for each session, while including a high-pass filter and an autoregressive model. Then, I was going to use the residuals from this model in a second model where all the conditions of interests are entered in one long column (concatenated across sessions). Does this make sense? Is there a better approach to this problem?
Thanks in advance.
Joon
