<p>The selected normal subjects are grouped by age with sufficiently large sample size and&nbsp;<span style="font-size: 1rem;">the means and standard deviations of the EEG time series and/or Frequency domain&nbsp;</span><span style="font-size: 1rem;">analyses are computed for each age group.&nbsp; &nbsp;Transforms are applied to approximate a&nbsp;</span><span style="font-size: 1rem;">Gaussian distribution of the EEG measures that comprise the means.&nbsp; &nbsp;Once&nbsp;</span><span style="font-size: 1rem;">approximation to Gaussian is completed, then Z scores are computed for each subject in the database and leave one out Gaussian Cross Validation is computed in order to arrive at an optimum Gaussian Cross —validation sensitivity.&nbsp; &nbsp;&nbsp;</span></p><p>Finally, the Gaussian validated norms are subjected to content and predictive validation procedures such as correlation with neuropsychological test scores and intelligence, etc. and also discriminant analyses and neural networks and outcome statistics, etc.&nbsp; &nbsp;The content validations are with respect to clinical measures such as intelligence, neuropsychological test scores, school achievement, clinical outcomes, etc.&nbsp; The predictive validations are with respect to the discriminative, statistical or neural network clinical classification accuracy.&nbsp; Both parametric and non parametric statistics are used to determine the content and predictive validity of a normative EEG database.&nbsp;</p>