Standardized varimax descriptors of event related potentials: Basic considerations

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Abstract
This paper describes a set of proposed standardized quantitative descriptors of event-related potentials, based upon principal component varimax analysis (PCVA). No claim is made that these mathematical descriptors correspond to discrete neurophysiological processes which generate the ERP. However, adoption and prospective evaluation of such a set of precise, standardized descriptors of the quantitative ERP may eventually result in advances like those which resulted from adoption of equally arbitrary standardized descriptors for QEEG. PCVA was performed on data from normal subjects and from groups of patients with a wide variety of psychiatric disorders (“Abnormals”). This yielded two sets of factor waveshapes, Normal and Abnormal, which were closely similar. Reconstruction of the normal and abnormal ERP data with either set of factors yielded almost identical allocation of variance. These results gave acceptable reassurance that factors derived from normal population could reasonably be used to describe ERP waveshapes from patients. The ERPs at each electrode of the 10/20 System in a “training group” of normal subjects were then reconstructed. The resulting distributions of factor scores were transformed to achieve Gaussianity. Mean values and standard deviations were obtained for the normative distribution of each factor score, the root mean square deviation, the residual and the absolute ERP power at each electrode. Individual ERPs could then be reconstructed with the normal factors, and the resulting factor scores rescaled to “probability of abnormal morphology” by Z-transformation. Statistical probability maps could be generated by using a color scale in standard deviation units. These methods were used to evaluate visual and auditory ERPs from an independent normal “test group” and the patients in the Abnormal sample. High specificity and sensitivity were obtained for many factor Z-scores. Multiple discriminant functions were constructed which separated normal from abnormal patients with high, replicable accuracy. Further development and testing of these descriptors may make them clinically useful.