Electroencephalographic fingerprints of statistical model selection by the brain

Abstract

An important issue in Neuroscience is identifying classes of models used by the brain to compress information from stimuli. We address this issue by proposing new electroencephalographic (EEG) experiments designed to study how the brain encodes statistical regularities from stochastic sequences of stimuli and the development of new statistical tools for functional data analysis, with a main focus in statistical model selection procedures for EGG data and functional data analysis.with both simulated and experimental EEG data obtained using this methodology which show that our method can discriminate between different conjectures about which information of the stimuli sequence is encoded in the electrical activity of the brain even if the information is not well described by a context tree model. The second method is a new graphical procedure for functional data. This procedure gives us a visual representation of the time domain in which two sets of functional data differ. We discuss how this graphical procedure can be used as an exploratory tool to better understand the inter-individual variance observed in experimental EEG data and how different participants encode the stimuli sequence information. (AU)