Feature Importance in Bayesian Assessment of Newborn Brain Maturity from
  EEG
  The methodology of Bayesian Model Averaging (BMA) is applied for assessment
of newborn brain maturity from sleep EEG. In theory this methodology provides
the most accurate assessments of uncertainty in decisions. However, the
existing BMA techniques have been shown providing biased assessments in the
absence of some prior information enabling to explore model parameter space in
details within a reasonable time. The lack in details leads to disproportional
sampling from the posterior distribution. In case of the EEG assessment of
brain maturity, BMA results can be biased because of the absence of information
about EEG feature importance. In this paper we explore how the posterior
information about EEG features can be used in order to reduce a negative impact
of disproportional sampling on BMA performance. We use EEG data recorded from
sleeping newborns to test the efficiency of the proposed BMA technique.
