Parametric and Non-Parametric Analysis of QEEG: Normative Database Comparisons in Electroencephalography, a Simulation Study on Accuracy

Resumen

Quantitative electroencephalography (QEEG) as a tool for the diagnosis of neurological and psychiatric disorders is receiving increased interest. While QEEG analysis is restricted to the scalp, the recent development of electromagnetic tomography (ET) allows the study of the electrical activity of all cortical structures. Electrical measures from a patient can be compared with a normative database derived from a large sample of healthy individuals. The deviance from the database norms provides a measure of the likelihood that the patient’s electrical activity reflects abnormal brain functioning. The focus of this article is a method for estimating such deviance. The traditional method based on z-scores (parametric) is reviewed and a new method based on percentiles (non-parametric) is proposed. The parametric and the non-parametric methods are compared using simulated data. The accuracy of both methods is assessed as a function of normative sample size and gaussianity for three different alpha levels. Results suggest that the performance of the parametric method is unaffected by sample size, given that the sample size is large enough (N > 100), but that non-gaussianity jeopardizes accuracy even if the normative distribution is close to gaussianity. In contrast, the performance of the non-parametric method is unaffected by non-gaussianity, but is a function of sample size only. It is shown that with N > 160, the non-parametric method is always preferable. Results will be discussed taking into consideration technical issues related to the nature of QEEG and ET data. It will be suggested that the sample size is the only constant across EEG frequency bands, measurement locations, and kind of quantitative measures. As a consequence, for a given database, the error rate of the non-parametric database is homogeneous; however, the same is not true for the parametric method.