Decrease of attention and an eventual microsleep of an artificial system operator is very dangerous and its early detection can prevent great losses. This chapter deals with a classification of states of vigilance based on analysis of an electroencefalographic activity of the brain. Preprocessing of data is done by the discrete Fourier transform. For the recognition radial basis functions (RBF), learning vector quantization (LVQ), multi-layer perceptron networks, k-nearest neighbor and a method based on Bayesian theory are used. Coefficients of bayes classifier are found using the maximum likelihood estimation. The experiments deal with analysis of human vigilance while their eyes are open. Then the reaction on visual stimuli is investigated. For this experiment 10 volunteers were repeatedly measured. The chapter shows that it is possible to classify vigilance in such conditions.
Decrease of attention and an possible micro-sleep of an artificial system operator is very dangerous and its early detection can prevent great losses. This article deals with a classification of States of vigilance based on the analysis of an electroencefalographic activity of the brain. Preprocessing of data is done by the Discrete Fourier Transform. For the recognition radial basis functions (RBF), a k-nearest neighbor and a method based on the Bayesian theory is used. Its coefficients are found using the maximum likelihood estimation. An experiment with recognition of 6 States of vigilance created according to reaction time is performed.