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The main aim in this paper is to study an algorithm of vigilance detection from a minimal number of EEG electrodes, easy to implement on programmable devices, to be used in ambulatory and real everyday life conditions. The connectionist unsupervised approach is summarized in this paper. From the unsupervised classification obtained, a connectionist supervised classification algorithm, the learning vector quantization (LVQ), is used for two different tasks. Firstly, the artefacted states are detected and removed. Secondly, the states deprived of artefacts are then classified in order to decide for the state of vigilance. Connectionist methods with supervised and unsupervised training were used to discriminate the EEG signals characterizing the vigilance states. An artificial neuronal model with a minimal architecture minimizes the complexity and allows implementation. It demonstrates that information, pertinent enough to characterize vigilance states, can be extracted from EEG signal recorded from a single electrode It should also be noted that the intervention of the expert is fundamental in this approach to differentiate nonartefacted vigilance states and artefacted vigilance states.