It has been repeatedly conjectured that the brain retrieves statistical regularities from stimuli. 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 chains with memory of variable length. It leads to a new experimental protocol in which sequences of auditory stimuli generated by a stochastic chain are presented to volunteers while electroencephalographic (EEG) data is recorded from their scalp. A new statistical model selection procedure for functional data is introduced and proved to be consistent. Applied to samples of EEG data collected using our experimental protocol it produces results supporting the conjecture that the brain effectively identifies the structure of the chain generating the sequence of stimuli. SEE REFERENCE
The data set is available on NeuroMat Database.
The algorithms for pre-processe and analyze the data set are available on folder EEGTreesAlgorithms. Instructions for the pre-processing are in the file README_Part1. Instructions for the analyses are in the file README_Part2.
The algorithms for the simulation study are available on folder Simulations. Instructions for the simulation is in the file README_Simulation
Retrieving a context tree from EEG data. Can be downloaded from arXiv:1602.00579, 2016.