The complexity of mental fatigue signals in healthy people is due to the absence of specific perturbations in the electroencephalographic activity, and by the singularity and variability of the cognitive profile of each individual. Identifying this mental state requires the analysis of several factors that involve the brain behavior in its regions in various frequency bands. In concern to the industry, mental fatigue compromises the efficiency of the production chain by affecting the perception (concentration and attention) of people, which increases the risk of accidents and production costs. Thus, monitoring the cognitive condition is necessary for the maintenance of the productive and cognitive performance of the evaluated subject. This work proposes the classification of fatigue using a competitive structure of Associative Neural Networks. This type of neural network allows to find the association between the input data and the reconstructed data from a compact architecture, being indicated for real-time applications. The characteristics vector used for classification is composed of the normalized information of three frequency bands (theta, beta and alpha) and four metrics that, according to the literature, differentiate mental states from electroencephalographic data in terms of Power Spectral Density. The results show the capacity and usability of autoassociative neural networks in patterns classification.
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