面向功能性用户体验质量评估的脑网络构建方法  

A brain network construction method for the assessment of functional quality of experience

作　　者：牛一帆 魏韬 张远[2] 翟广涛 邬霞[1] Niu Yifan;Wei Tao;Zhang Yuan;Zhai Guangtao;Wu Xia(School of Artificial Intelligence,Beijing Normal University,Beijing 100875,China;State Key Laboratory of Media Convergence and Communication,Communication University of China,Beijing 100024,China;School of Electronics,Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]北京师范大学人工智能学院,北京100875 [2]中国传媒大学媒体融合与传播国家重点实验室,北京100024 [3]上海交通大学电子信息与电气工程学院,上海200240

出　　处：《中国图象图形学报》2024年第9期2793-2805,共13页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62227801)。

摘　　要：目的新兴视频服务的功能参数设置将会直接影响到用户的认知状态,进一步影响用户体验质量,称为功能性用户体验质量(functional quality of experience,fQoE)。脑电信号蕴含丰富的大脑活动信息,能够揭示复杂脑活动中的脑网络模式,为fQoE提供可靠的评估依据。为此,本文首次提出了一个基于脑电的脑网络构建方法以评估fQoE,并研究fQoE背后的神经机制。方法首先,通过改变功能参数诱发不同水平的fQoE,并同步收集脑电数据;然后,从脑电数据中提取单电极和多电极特征并以图结构进行融合,用以全面表征用户使用视频服务时的大脑状态;最后,使用基于自注意力图池化的脑网络构建模型来识别对fQoE敏感的脑网络,为fQoE提供可解释性,并进行分类以完成fQoE评估。结果本文以弹幕视频服务的弹幕覆盖率这一功能参数为例验证了方法的科学性和可行性。实验表明,提出的评估方法在多种视频类型的fQoE评估中均达到了满意的效果,最佳识别准确率分别为86%(鬼畜类)、81%(科技类)、80%(舞蹈类)、82%(影视类)和84%(音乐类)。结论来自fQoE相关的脑网络分析结果表明,额极、额中回、顶叶和颞叶的脑连接数量减少预示着观看弹幕视频的fQoE更高,即观看体验更好,同时也证明了功能参数通过影响人的脑状态进一步导致了fQoE的改变。本文的评估方法为fQoE的精确评估和视频服务功能参数的优化提供了来自神经生理学的定量工具和理论依据。Objective The rapid development of multimedia technology enables emerging video services,such as bullet chatting video and virtual idol.Technical parameters from network and application layers affect user’s quality of experi⁃ence(QoE).In addition,the QoE changes when various adjustable functional parameters of these video services are modi⁃fied,which we call QoE influenced by functional parameters(functional QoE,fQoE).Changes in functional parameters influence human cognition and emotion,and thus,fQoE is almost entirely decided by human subjective perceptions.Inferring directly from parameter design,which is difficult,makes fQoE modeling challenging.The success of the video ser⁃vices depends entirely on user ratings,and understanding fQoE and the reason behind its generation is crucial for service providers.Studies using questionnaire and interview methods have been conducted to understand users’perceptions of functional parameters.However,subjects may be influenced by external criteria and social desirability,which potentially result in bias of the collected results.The above methods cannot perform the quantitative assessment of fQoE nor provide scientific evidence with interpretability.Electroencephalography(EEG)signals contain a wealth of information about brain activity,and EEG features can reveal brain network patterns during complex brain activity.Studies have used EEG as a powerful tool to assess the QoE influenced by technical parameters(tQoE)and uncovered relevant EEG single-electrode features,which revealed the correlation between tQoE and basic human perceptual functions and demonstrated the strong potential of EEG for fQoE assessment.However,fQoE may involve higher-order human cognitive functions that require interactions between multiple brain regions,such as social communication and emotion,whose complex relationships are difficult to be represented by single-electrode features.To address the limitations of the above studies,this paper presents an fQoE assessment model based on the EEG tec

关 键 词：新兴视频服务 功能性用户体验质量(fQoE) 脑电信号(EEG) 脑网络构建 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]