EEGretrieving

Dataset and code  of the article "Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data"

Authors: Noslen Hernández, Aline Duarte, Guilherme Ost, Ricardo Fraiman, Antonio Galves, and Claudia D. Vargas

Date: 2020-01-29

Description: 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 {\sl 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.

[Raw data] [Preprocessed data] [Source code] [README file]