Quantitative EEG Normative Databases: Validation and Clinical Correlation

SUMMARY

The quantitative digital electroencephalogram (QEEG) was recorded from 19 scalp locations from 625 screened and evaluated normal individuals ranging in age from two months to 82 years. After editing to remove artifact, one-year to five-year groupings were selected to produce different average age groups. Estimates of gaussian distributions and logarithmic transforms of the digital EEG were used to establish approximate gaussian distributions when necessary for different variables and age groupings. The sensitivity of the lifespan database was determined by gaussian cross-validation for any selection of age range in which the average percentage of Z-scores ± 2 standard deviations (SD) equals approximately 2.3% and the average percentage for ± 3 SD equals approximately 0.13%. It was hypothesized that measures of gaussian cross-validation of Z-scores is a common metric by which the statistical sensitivity of any normative database for any age grouping can be calculated. This theory was tested by computing eyes-closed and eyes-open average reference and current source density norms and independently cross-validating and comparing to the linked ears norms. The results indicate that age-dependent digital EEG normative databases are reliable and stable and behave like different gaussian lenses that spatially focus the electroencephalogram. Clinical correlations of a normative database are determined by content validation and correlation with neuropsychological test scores and discriminate accuracy. Non-parametric statistics were presented as an important aid to establish the alpha level necessary to reject a hypothesis and to estimate Type I and Type II errors, especially when there are multiple comparisons of an individual's EEG to any normative EEG database.

KEYWORDS:

• EEG normative databases
• gaussian distributions
• error estimates