Cognitive state prediction using an EM algorithm applied to Gamma distributed data

Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:7819-24. doi: 10.1109/EMBC.2015.7320205.


Behavioral tests are widely used to quantify features of cognitive processing. For a large class of behavioral signals, the observed variables are non-Gaussian and dynamic; classical estimation algorithms are ill-suited to modeling such data. In this research, we propose a mathematical framework to predict a cognitive state variable related to behavioral signals, which are best modeled using a Gamma distribution. The proposed algorithm combines a Gamma Smoother and EM algorithm in the prediction process. The algorithm is applied to reaction time recorded from subjects performing a Multi-Source Interference Task (MSIT) to dynamically quantify their cognitive flexibility through the course of the experiment.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Cognition / physiology*
  • Humans
  • Models, Theoretical
  • Psychophysics / statistics & numerical data
  • Reaction Time*
  • Statistical Distributions*