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 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.