Hidden context tree modeling of EEG data

In this talk a new class of stochastic processes is presented: the Hidden Context Tree Models (HCTM). This class gives a natural framework to mathematically address the neurobiological conjecture about the ability of the brain to identify the structure of a random source. Statistical model selection in a suitable subclass of HCTMs can provide experimental evidence supporting this conjecture. A case study with EEG data will be also presented. This is a joint work with A. Duarte, R. Fraiman, G. Ost and C. Vargas.

Presented at MathStatNeuro Workshop, Laboratoire J. A. Dieudonné, Nice, France, September 10, 2015.

Author: Antonio Galves.

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