  It has been classically conjectured that the brain compresses data by
assigning probabilistic models to sequences of stimuli. An important issue
associated to this conjecture is what class of models is used by the brain to
perform its compression task. We address this issue by introducing a new
statistical model selection procedure aiming to study the manner by which the
brain performs data compression. Our procedure uses context tree models to
represent sequences of stimuli and a new projective method for clustering EEG
segments. The starting point is an experimental protocol in which EEG data is
recorded while a participant is exposed to auditory stimuli generated by a
stochastic chain. A simulation study using sequences of stimuli generated by
two different context tree models with EEG segments generated by two distinct
algorithms concludes this article.
