Processing EEG Signals Towards the Construction of a User Experience Assessment Method

This paper proposes a neural network to identify pleasant and unpleasant emotions from recorded electroencephalography (EEG) signals, towards the construction of a method to assess user experience (UX). EEG signals were obtained with an Emotiv EEG device. The input data was recorded during the presentation of visual stimulus that induce emotions known a priori. The EEG signals recorded were preprocessed to enhance the differences and then used to train and validate a Patternet neural network. The results indicate that the neural network provides an accurate rate of 99.61 % for 258 preprocessed signals.

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