I tested reactions to pictures under five conditions (neutral, high status, low status, high adhesion, low adhesion), using an EEG of 16 channels. The pictures were presented in random order for 15 times under each condition. So at best I have data from 75 trials for each subject in each channel. But as some electrodes sometimes made me some problems, I can't use the data from all of the EEG channels for all of the subjects. And as an EEG is sensitive to movements of the head, especially to eye movements, I furthermore had to reject some of the trials in all of the EEG channels for some of the subjects. So, on the whole, I neither have the same number of channels for each subject nor the same number of trials in the lasting channels for all of the subjects and therefore an unbalanced design.
I split-up the data in 12 time segments per EEG channel and condition and I'm interested in the divergent effects of the conditions on the EEG waves in each of these time segments. So, I want to compare the data (trials) from all the subjects for condition "status high" in channel 1 for time segment 1 with the data from all these subjects for condition "status low" in channel 1 for time segment 1. And the data from all the subjects for conditions "status high" in channel 1 for time segment 2 with the data from all these subjects for condition "status low" in channel 1 for time segment 2. And so on, until the pairwise comparison for time segment 12 in that first channel. And then, I want to do the same with channel 2 and channel 3 and so on, until channel 16. And then, I do the same with "adhesion high" vs. "adhesion low" for all of the channels.
Could I run these tests with the help of a Linear Mixed Model? What would be the fixed and random effects? Or would a multivariate approach be the better choice?
I would be so grateful, if you could sacrifice a little bit of your time for my problem. Please excuse my maladroit wording.