
Accession Number : AD0714174
Title : On Feature Reduction with Application to Electroencephalograms.
Descriptive Note : Technical rept.,
Corporate Author : HARVARD UNIV CAMBRIDGE MASS DIV OF ENGINEERING AND APPLIED PHYSICS
Personal Author(s) : Prabhu,Karkal Pulkeri Sheshagiri
Report Date : SEP 1970
Pagination or Media Count : 156
Abstract : The report deals with the feature reduction problem in pattern classification, with application to electroencephalograph (EEG) signals. The problem under consideration is that of discriminating between two kinds of signalsspontaneous EEG and EEG driven by photic stimuli at the alpha frequency. Since an EEG record represents a large amount of data, efficient feature reduction methods are required to pick out a few features which are significant for discrimination purposes. The first two chapters are of an introductory nature describing statistical feature reduction methods given in the literature and some relevant facts about EEG signals. The third chapter develops a nonparametric feature reduction procedure based on a distance measure. The fourth chapter develops a random process model for the two kinds of EEG signals. The signal is essentially represented as a sinusoid at the alpha frequency with random amplitude and phase modulation. It is seen that the statistical properties predicted by the model agree closely with the observed results. In the fifth chapter, the model is employed for feature reduction and pattern classification. The model provides a four dimensional vector of sufficient statistics, which contains all the information necessary for discrimination purposes. (Author)
Descriptors : (*ELECTROENCEPHALOGRAPHY, PATTERN RECOGNITION), NERVOUS SYSTEM, PHYSIOLOGY, MODELS(SIMULATIONS), ELECTROPHYSIOLOGY, SPECTRUM SIGNATURES, STATISTICAL ANALYSIS, INFORMATION THEORY, SIGNALS, THESES
Subject Categories : Anatomy and Physiology
Cybernetics
Distribution Statement : APPROVED FOR PUBLIC RELEASE