Since emerging video services can provide emotional and social value to users, the setting of their functional parameters directly affects human cognitive and affective states, further influencing video services’ quality of experience (QoE), which we call functional QoE (fQoE). FQoE is highly dependent on human subjective perceptions and the reasons for its generation are important for service providers to optimize video services. However, existing fQoE research methods are unable to perform quantitative assessment and lack interpretability. Electroencephalogram (EEG) signals have the advantage of being difficult to disguise, and contain rich brain activity information, gaining more attention from researchers nowadays. Based on EEG, we propose an interpretable model to evaluate fQoE, and the model is tested on a self-built dataset for bullet chatting video (BCV) service. Our model can effectively fuse single electrode and multi-electrode features from EEG, and introduces Graph-based Brain-area Perception Network (GBPN) for extracting fQoE sensitive brain areas, achieving satisfactory results. We find brain areas associated with fQoE caused by different functional parameters of BCV. To sum up, our fQoE model enables quantitative assessment with neurophysiological interpretability of fQoE, providing a scientific basis for the optimization and development of video services.

中文翻译：

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迈向更好的视频服务：基于脑电图的可解释模型，用于功能体验质量评估

由于新兴的视频业务可以为用户提供情感和社会价值，其功能参数的设置直接影响人类的认知和情感状态，进一步影响视频业务的体验质量（QoE），我们称之为功能QoE（fQoE）。 FQoE高度依赖于人类的主观感知，其产生的原因对于服务提供商优化视频服务非常重要。然而，现有的fQoE研究方法无法进行定量评估且缺乏可解释性。脑电图（EEG）信号具有难以伪装、包含丰富的大脑活动信息等优点，越来越受到研究者的关注。基于EEG，我们提出了一个可解释的模型来评估fQoE，并在自建的弹幕视频（BCV）服务数据集上对该模型进行了测试。我们的模型可以有效融合脑电图的单电极和多电极特征，并引入基于图的脑区域感知网络（GBPN）来提取fQoE敏感脑区域，取得了满意的结果。我们发现与 fQoE 相关的大脑区域是由 BCV 的不同功能参数引起的。综上所述，我们的fQoE模型能够对fQoE进行具有神经生理学可解释性的定量评估，为视频服务的优化和发展提供科学依据。