In your setting, subject and itemstimulus seem to be fully crossed random grouping factors - since each subject sees each stimulus and (I am assuming) you are using the subjects and stimuli included in your studies to represent all the subjects and all the stimuli you wish to generalize your study findings to.
The key word here is grouping - for your model to be a linear mixed effects model (lmer), each subject by stimulus combination should act like a container which holds together a group of values for your measure outcome. All the values of measure that belong to the same container are more similar to each other than values that belong to different containers, as they are subjected to the same subject-level and stimulus-level influences (presuming these influences are constant over time).
The group of values in a specific container could arise, for instance, if you record the value of measure at several time points for each subject by stimulus combination, or under two or more different conditions, etc.
If you only have one value of measure per subject by stimulus combination, then you're dealing with a linear model (lm). There is no grouping of observations according to each subject per stimulus combination, so there are no random grouping factors which means there aren't any effects that can vary randomly across combinations of levels of the grouping factors (i.e., random effects). If there aren't any random effects, there can't be a mixed effects model, as such a model would require both fixed and random effects to be part of it!
If you do have multiple values of measure per container (i.e., subject by stimulus combination), then your model can include subject-level predictors (e.g., subject gender, subject age) and/or stimulus-level predictors (e.g., stimulus category).