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Dear Muhammad, For epoching there should be no problem. You can specify things that are not present in the data and they will just not be used. To make things consistent across subjects you might want to add empty conditions with nrepl of 0. This can be done with the contrast function if you put 0 as your contrast weights. You should then use unweighted option but afterwards use the repl method to set the number of replications for that condition to 0. Also make sure that you sort conditions in the same order for all subjects (you can just put sorting (spm_eeg_sort_conditions) for all subjects before applying any contrasts). The use of dummy condition will not always make sense depending on the kind of contrast so you should be careful to keep things meaningful. It should work for something like grand-averaging but I haven't tried it myself. Best, Vladimir On Thu, Feb 16, 2012 at 3:23 PM, Muhammad Adeel Parvaz <[log in to unmask]> wrote: > Hello SPMers, > > I am working with an EEG data set which was collected on a decision making > task. In this task, participants had an option to make a decision by > pressing one of four buttons. The feedback they get is based on the button > they pressed. The events are also marked (in the EEG data) accordingly. Now, > it is possible that some participants might not have pressed a certain > button at all throughout the task, so those markers are not present in the > data, but for others they do. > > My questions are: > > 1. How can I include the epoching in a script that will accommodate these > variations in subject's responses? > 2. Is there a way to keep a consistent 'condition' sequence for all the > participant's averaged ERP files so that later steps in which we create > contrasts between these conditions are simpler and scriptable? > > > Thanks > Muhammad