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.

翻译：在人际交互中，情感识别至关重要。因此，基于计算机视觉的自动情感识别方法当前受到广泛研究。处理多通道脑电图（EEG）信息是自动情感识别中研究最多的方法之一。本文提出了一种基于情感计算的体验质量（QoE）预测新模型。为验证该模型，采用了公开数据集，该数据集包含EEG、心电图（ECG）和呼吸数据，聚焦于多媒体QoE评估场景。保留EEG数据，以三秒观测窗口计算差分熵和功率谱密度。提取这两个特征以训练多种深度学习模型，探究通过五种不同因子预测QoE的可能性。比较各模型性能，并对最优模型进行优化以提升结果。基于LSTM的模型取得了最佳效果，F1分数达68%至78%。模型及其特征分析表明，Delta频段需求最低，两个电极重要性较高，而另外两个电极对模型性能影响极小。