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.
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.