Retrieving a context tree from EEG data

A. Duarte, R. Fraiman, A. Galves, G. Ost, C. Vargas

It has been repeatedly conjectured that the brain retrieves statistical regularities from stimuli, so that their structural features are separated from noise. Here we present a new statistical approach allowing to address this conjecture. This approach is based on a new class of stochastic processes driven by context tree models. Also, it associates to a new experimental protocol in which structured auditory sequences are presented to volunteers while electroencephalographic signals are recorded from their scalp. A statistical model selection procedure for functional data is presented to analyze the electrophysiological signals. This procedure is proved to be consistent. Applied to samples of electrophysiological trajectories collected during structured auditory stimuli presentation, it produces results supporting the conjecture that the brain effectively identifies the context tree characterizing the source.

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