  Using a new probabilistic approach we model the relationship between
sequences of auditory stimuli generated by stochastic chains and the
electroencephalographic (EEG) data acquired while 19 participants were exposed
to those stimuli. The structure of the chains generating the stimuli are
characterized by rooted and labeled trees whose leaves, henceforth called
contexts, represent the sequences of past stimuli governing the choice of the
next stimulus. A classical conjecture claims that the brain assigns
probabilistic models to samples of stimuli. If this is true, then the context
tree generating the sequence of stimuli should be encoded in the brain
activity. Using an innovative statistical procedure we show that this context
tree can effectively be extracted from the EEG data, thus giving support to the
classical conjecture.
