Video streaming through the Internet is abundant nowadays. While video quality is continuously demanded, monitoring users’ quality of experience (QoE) is essential when watching video contents. QoE can be evaluated directly through subjective assessment which is the human ground truths; however, such assessment is generally expensive and time consuming, and cannot be implemented in real time. QoE can also be evaluated by video quality models; however, the evaluation is fully based on video contents but human physical states cannot be taken into account. To tackle the limitations, detection of a prominent electroencephalography (EEG) signal feature namely P300 correlated to QoE can be used, when users are viewing videos. P300 is a positive deflection pulse that appears around 300 ms after a significant video distortion appears. QoE can be indicated by P300 pulses. However, the captured EEG signal is generally contaminated with noise. Strong noise generates P300 although video carries no distortion. Hence, detections of P300 patterns are not accurate. In this paper, a double classifier consisting of a first and second classifier is proposed. The first classifier attempts to determine whether the captured EEG feature is abnormal or not, where the abnormal caption behaves opposite to the normal P300 characteristic when showing the distorted video. The second classifier is developed to perform classifications for either normal or abnormal features. We evaluate the performance of the proposed double classifier based on the EEG samples, which are captured when showing video stimuli to participants. The proposed classifier is implemented by the support vector machine and logistic regression, which are commonly used for detection of EEG patterns and are computationally much simpler than deep learning. The performance of the proposed classifier is compared to those of the single classifiers, which determine the QoE directly when the EEG signal is given. Cross-validations showed that generally more than 5% improvement can be achieved by the proposed double classifier. Statistical tests indicate that the proposed double classifier can generally obtain better classification rates than solely using the single classifier at a 97.5% confidence level.

如今，通过互联网流式传输的视频非常丰富。尽管对视频质量的要求不断提高，但是在观看视频内容时，监视用户的体验质量（QoE）至关重要。可以通过主观评估直接评估QoE，这是人为基础的事实；然而，这种评估通常是昂贵且费时的，并且不能实时实施。QoE也可以通过视频质量模型进行评估；但是，评估完全基于视频内容，但不能考虑人体状况。为了解决这些限制，当用户观看视频时，可以使用突出的脑电图（EEG）信号特征即与QoE相关的P300的检测。P300是正偏转脉冲，出现明显的视频失真后约300毫秒出现。QoE可由P300脉冲指示。然而，捕获的EEG信号通常被噪声污染。尽管视频没有失真，但强烈的噪音会产生P300。因此，P300模式的检测不准确。本文提出了一种由第一分类器和第二分类器组成的双重分类器。第一分类器试图确定所捕获的EEG特征是否异常，其中当显示失真的视频时，异常字幕的行为与正常的P300特征相反。第二分类器被开发用于执行正常或异常特征的分类。我们基于EEG样本评估建议的双分类器的性能，这些样本是在向参与者显示视频刺激时捕获的。所提出的分类器是通过支持向量机和逻辑回归实现的，后者通常用于检测EEG模式，并且在计算上比深度学习要简单得多。将提出的分类器的性能与单个分类器的性能进行比较，后者在给出EEG信号时直接确定QoE。交叉验证表明，提出的双分类器通常可以实现5％以上的改进。统计测试表明，与仅使用单个分类器在97.5％的置信度下相比，建议的双重分类器通常可以获得更好的分类率。将提出的分类器的性能与单个分类器的性能进行比较，后者在给出EEG信号时直接确定QoE。交叉验证表明，提出的双分类器通常可以实现5％以上的改进。统计测试表明，与仅使用单个分类器在97.5％的置信度下相比，建议的双重分类器通常可以获得更好的分类率。将提出的分类器的性能与单个分类器的性能进行比较，后者在给出EEG信号时直接确定QoE。交叉验证表明，提出的双分类器通常可以实现5％以上的改进。统计测试表明，与仅使用单个分类器在97.5％的置信度下相比，建议的双重分类器通常可以获得更好的分类率。