ABSTRACT
Neural processing of sensory stimuli can be studied using EEG by estimation of the evoked potential using the averages of large sets of trials. However, it is not always possible to include all stimulus parameters in a conventional analysis, since this would lead to an insufficient amount of trials to obtain the evoked potential by averaging. Linear mixed models use dependencies within the data to combine information from all data for the estimation of the evoked potential. In this work, it is shown that in multi-stimulus EEG data the quality of an evoked potential estimate can be improved by using a linear mixed model. Furthermore, the linear mixed model effectively deals with correlation between parameters in the data and reveals the influence of individual stimulus parameters.
