  In human interactions, emotion recognition is crucial. For this reason, the
topic of computer-vision approaches for automatic emotion recognition is
currently being extensively researched. Processing multi-channel
electroencephalogram (EEG) information is one of the most researched methods
for automatic emotion recognition. This paper presents a new model for an
affective computing-driven Quality of Experience (QoE) prediction. In order to
validate the proposed model, a publicly available dataset is used. The dataset
contains EEG, ECG, and respiratory data and is focused on a multimedia QoE
assessment context. The EEG data are retained on which the differential entropy
and the power spectral density are calculated with an observation window of
three seconds. These two features were extracted to train several deep-learning
models to investigate the possibility of predicting QoE with five different
factors. The performance of these models is compared, and the best model is
optimized to improve the results. The best results were obtained with an
LSTM-based model, presenting an F1-score from 68% to 78%. An analysis of the
model and its features shows that the Delta frequency band is the least
necessary, that two electrodes have a higher importance, and that two other
electrodes have a very low impact on the model's performances.
