The following drawing from a forthcoming book is posted by popular demand. It illustrates the basic concepts of static versus dynamic z-scores. Note that z-scores based on the static distribution will be larger, but in the same direction, as z-scores based on the dynamic distribution. Both BrainAvatar/BrainDX and NeuroGuide Deluxe use the static distribution for z-scores. The ANI Z DLL uses the dynamic distribution for z-scores. Therefore, “BrainAvatar/BrainDX live maps should (and do) look like NeuroGuide Deluxe maps.”
The following illustration from Collura et al. (2009) and produced by Dr. Thatcher shows that the only difference between the static and dynamic z-scores is the denominator, which accounts for the difference in variance. By this analysis, it is clear that the target values (means) for both types of z-scores must be identical:
Collura, T.F., Thatcher, R.W., Smith, M.L., Lambos, W.A., and C.R. Stark (2009) EEG Biofeedback training using Z-scores and a normative database, in: (Evans, W., Budzynski, T., Budzynski, H., and A. Arbanal, eds) Introduction to QEEG and Neurofeedback : Advanced Theory and Applications, Second Edition. New York: Elsevier.
Conclusion: It has been shown that the relationships between three types of norms are as follows:
population dynamic data show the largest distribution, hence will produce the lowest z-scores.
population static data will produce higher z-scores, but will have the same target (mean) values as the population dynamic references
individual dynamic data will have a different mean for each individual, and a distribution that is smaller than the population dynamic data. Z-scores based on this referencew will reflect how the client compares with the individual from whom the reference data were gathered.
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