Guillermo Ramos-Auñón, Inma Mohino-Herranz, Héctor A Sánchez-Hevia, Cosme Llerena-Aguilar, and David Ayllón
Stress detection, EEG, Biomedical signal processing
In this paper, we propose a computationally-efficient EEG-based stress detection that uses only two non-invasive electrodes. The system is designed to classify between two situations: high stress level or low stress level. A linear classifier is trained using supervised learning using a subset of features that has been selected among a larger proposed set of features, using a tailored feature selection algorithm. The proposed algorithm has been evaluated with subjects playing skill games, obtaining errors of 19.2% in the train set and 29.2% in the test set.
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