Attention decrease and an eventual micro-sleep of an artificial system operator is very dangerous and its early detection can prevent great losses. This work deals with an early detection of micro-sleep based on analysis of an electroencefalographic activity of tlie brain. There are classic spectral methods - the Discrete Fourier Transform and parametric methods - autoregressive models used for signal processing here. An influence of a band pass filter characteristic on classification is investigated. For the detection of the micro-sleep multi-layer perceptron, radial basis function (RBF) and the learning vector quantization (LVQ) neural networks are used. The k-nearest neighbor as a representative of non-parametric methods is examined. The last method used here is based on the Bayesian theory and its coefficients are found using the maximum likelihood estimation.
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.
Detection and early prediction of hypnagogium based on the EEG analysis is a very promising way how to deal with different states of vigilance. We are dealing with the EEG signal using different methodology mainly based on the spectral analysis such as Fourier transform, autoregressive models and also different kinds of filters. For the detection of hypnagogium we are using methods such as bayes classifier, nearest-neighbor and methods of neural networks. We are performing the analysis of EEG to recognize and classify the hypnagogium.
This paper introduces the cooperation of a virtual car simulator and
EEG measurements to test a human driver’s behavior in demanding situations. After a short explanation of the main principles and tasks of EEG measurements, basic concepts of our experiments are presented. The following part is devoted to problems and solutions concerning the physical model, graphical and other aspects of our simulator. At the end of the article various measuring procedures are presented.
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.