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Topographic mapping of brain electrical activity has become a commonly used method in the clinical as well as research laboratory. To enhance analytic power and accuracy, mapping applications often involve statistical paradigms for the detection of abnormality or difference. Because mapping studies involve many measurements and variables, the appearance of(More)
We describe a method for the diagnosis of dyslexia based upon a study of electroencephalographic and evoked potential data recorded from 13 normal and 11 dyslexic boys. Measurements were made from topographic maps of brain electrical activity recorded during resting and activated testing conditions. Using a statistically based technique, we developed rules(More)
We illustrate the application of significance probability mapping (SPM) to the analysis of topographic maps of spectral analyzed EEG and visual evoked potential (VEP) activity from patients with brain tumors, boys with dyslexia, and control subjects. When the VEP topographic plots of tumor patients were displayed as number of standard deviations from a(More)
Electroencephalographic (EEG) and evoked potential data were recorded during behavioral testing from 8 dyslexic and 10 normal boys aged 9 to 11 years. Topographic mapping of their brain electrical activity revealed four discrete regions of difference between the two groups involving both hemispheres, left more than right. Aberrant dyslexic physiology was(More)
Principal components analysis (PCA) was performed on the 1536 spectral and 2944 evoked potential (EP) variables generated by neurophysiologic paradigms including flash VER, click AER, and eyes open and closed spectral EEG from 202 healthy subjects aged 30 to 80. In each case data dimensionality of 1500 to 3000 was substantially reduced using PCA by(More)
In this paper we describe attempts at building a robust model for predicting the length of survival of patients with colorectal cancer. The aim of the research, reported in this paper, is to study the effective utilisation of artificial intelligence techniques in the medical domain. We suggest that an important research objective of proponents of(More)
Principal components transformation may be used to explore the structure of a p-dimensional data set. It is difficult to detect inhomogeneities in a data set of multivariate variables by mere visual inspection of the numerical data. Plotting each variable's distribution is often either impractical, due to the number of variables involved, or might fail to(More)
Multivariate analysis is commonly used to "prove" the existence of significant, if frequently small, differences between samples. Methods with numerical examples are presented for three test statistics used in multivariate analysis to assess the chance of an error of the first kind, alpha, that the differences observed are merely the result of chance.(More)
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