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.