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I'm running an EEG experiment using a modified auditory mismatch negativity (MMN) design, and I'm wondering if anyone can tell me the best method for data analysis (and recommend any stats programs/packages for this purpose as well).

The experiment will only have a few (6-8) subjects with a high number of trials per subject (~700). The matched condition makes up 75% of the trials. The stimuli are selected from a closed set of 10 items, with an equal frequency of items in the matched/mismatched conditions.

One of the differences between this study and normal auditory MMN tasks is that the expected/unexpected stimuli vary between trials with the expectation set up on a trial-by-trial basis. Therefore, it's possible that the difference in event-related potentials (ERPs) to expected/unexpected stimuli will be averaged out due to small variations in response to these changing stimuli.

Does someone out there know the best EEG data processing and analysis techniques to detect the small ERP in response to auditory MMN stimuli? Can anyone recommend techniques to avoid averaging out small ERPs whose latencies might vary?