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收稿：2023-07-21，
修回：2023-12-16，
纸质出版：2024-09-16
移动端阅览
目的
2
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
2
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
2
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
2
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
2
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 experience （QoE）. In addition， the QoE changes when various adjustable functional parameters of these video services are modified， 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 services 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 technology and investigates the neural mechanisms behind fQoE.
Method
2
First， an EEG dataset for fQoE assessment was constructed. To reduce the influence of subjects’ personal preferences on the experimental results， we ensured that the stimulus materials contained five types of videos （auto-tune， technology， dance， film， and music）. Different levels of fQoE were induced by changing the functional parameters， and the EEG data of subjects were collected simultaneously. Second， on top of single-electrode features， we additionally extracted multielectrode features （i.e.， functional connectivity features） and fused both types in the form of graph. The EEG electrodes were represented as nodes on the graph， the single-electrode features as node features， and the functional connectivity as edges of the graph. The weights of edges represent the strength of functional connectivity， and were used in the comprehensive characterization of the user’s brain state when using video service. Finally， self-attention graph pooling mechanism was introduced to construct a brain-network construction model to identify fQoE levels. The graph pooling layer can enlarge the field of view to the whole graph structure during the training process， retain key nodes， and compose new graphs to render the model with the capability to capture key brain networks. We further explored the neurophysiological principles behind it and provided theoretical support for the improvement of emerging video services.
Result
2
With the bullet chatting video， a new type of video service， as an example， this paper explored the fQoE affected by the functional parameter of bullet chatting coverage and the neurophysiological principles behind it. The finding verified the scientific validity and feasibility of the method. Experiments revealed that the assessment method proposed in this paper achieved satisfactory results in the fQoE evaluation of multiple video types， with the best recognition accuracy of 86% （auto-tune）， 80% （technology）， 80% （dance）， 82% （film）， and 84% （music）. Compared with existing machine learning and deep learning models， our method achieves the best recognition accuracy. The results on fQoE-related brain network analysis reveal that the number of brain connections in the frontal， parietal， and temporal lobes decreased， which indicates a attained fQoE for viewing bullet chat videos， i.e.， a better viewing experience. This result also implies that functional parameters further lead to changes in the fQoE by affecting the human brain state. Specifically， the brain connection between the frontal and temporal lobes is related to speech information processing， the parietal lobe brain connection to visual information processing， the strength of frontal lobe brain connection to cognitive load levels， and its asymmetry to emotions and motivation.
Conclusion
2
In this study， we initially presented an EEG-based fQoE assessment model to evaluate the fQoE levels using a brain-network construction model based on a self-attention graph pooling mechanism and analyzed the neurophysiological rationale behind it. The assessment method introduced in this paper serves as a quantitative tool and theoretical basis from neurophysiology for the accurate assessment of fQoE and optimization of functional parameters of video services.
Acqualagna L ， Bosse S ， Porbadnigk A K ， Curio G ， Müller K R ， Wiegand T and Blankertz B . 2015 . EEG-based classification of video quality perception using steady state visual evoked potentials （SSVEPs） . Journal of Neural Engineering ， 12 （ 2 ）： # 026012 ［ DOI： 10.1088/1741-2560/12/2/026012 http://dx.doi.org/10.1088/1741-2560/12/2/026012 ］
Akhtar Z and Falk T H . 2017 . Audio-visual multimedia quality assessment： a comprehensive survey . IEEE Access ， 5 ： 21090 - 21117 ［ DOI： 10.1109/ACCESS.2017.2750918 http://dx.doi.org/10.1109/ACCESS.2017.2750918 ］
Baldwin C L and Penaranda B N . 2012 . Adaptive training using an artificial neural network and EEG metrics for within- and cross-task workload classification . NeuroImage ， 59 （ 1 ）： 48 - 56 ［ DOI： 10.1016/j.neuroimage.2011.07.047 http://dx.doi.org/10.1016/j.neuroimage.2011.07.047 ］
Bampis C G ， Li Z ， Katsavounidis I ， Huang T Y ， Ekanadham C and Bovik A C . 2018 . Towards perceptually optimized end-to-end adaptive video streaming ［EB/OL］. ［ 2023-07-21 ］. https://arxiv.org/pdf/1808.03898.pdf https://arxiv.org/pdf/1808.03898.pdf
Baumeister R F ， Bratslavsky E ， Finkenauer C and Vohs K D . 2001 . Bad is stronger than good . Review of General Psychology ， 4 （ 5 ）： 323 - 370 ［ DOI： 10.1037/1089-2680.5.4.323 http://dx.doi.org/10.1037/1089-2680.5.4.323 ］
Bilibili Inc . Global Offering Prospectus . 2021 . ［EB/OL］. ［ 2023-07-21 ］. https://www1.hkexnews.hk/listedco/listconews/sehk/2021/0318/2021031800173.pdf https://www1.hkexnews.hk/listedco/listconews/sehk/2021/0318/2021031800173.pdf
Bosse S ， Acqualagna L ， Samek W ， Porbadnigk A K ， Curio G ， Blankertz B ， Müller K R and Wiegand T . 2018 . Assessing perceived image quality using steady-state visual evoked potentials and spatio-spectral decomposition . IEEE Transactions on Circuits and Systems for Video Technology ， 28 （ 8 ）： 1694 - 1706 ［ DOI： 10.1109/TCSVT.2017.2694807 http://dx.doi.org/10.1109/TCSVT.2017.2694807 ］
Carlton J ， Brown A ， Jay C and Keane J . 2021 . Using interaction data to predict engagement with interactive media ［EB/OL］. ［ 2023-07-21 ］. https://arxiv.org/pdf/2108.01949.pdf https://arxiv.org/pdf/2108.01949.pdf
Chan K Y ， Arndt S and Engelke U . 2020 . A novel strategy for classifying perceived video quality using electroencephalography signals . Engineering Applications of Artificial Intelligence ， 92 ： # 103692 ［ DOI： 10.1016/j.engappai.2020.103692 http://dx.doi.org/10.1016/j.engappai.2020.103692 ］
Chin Z Y ， Zhang X ， Wang C C and Ang K K . 2018 . EEG-based discrimination of different cognitive workload levels from mental arithmetic // Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society （EMBC） . Honolulu， USA ： IEEE ， 2018 ： 1984 - 1987 ［ DOI： 10.1109/EMBC.2018.8512675 http://dx.doi.org/10.1109/EMBC.2018.8512675 ］
Datta S and Ghosh T . 2022 . Linking service quality， value， and loyalty in the V-o-D service context through a review of literature . Cogent Business and Management ， 9 （ 1 ）： # 2143311 ［ DOI： 10.1080/23311975.2022.2143311 http://dx.doi.org/10.1080/23311975.2022.2143311 ］
Delorme A and Makeig S . 2004 . EEGLAB： an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis . Journal of Neuroscience Methods ， 134 （ 1 ）： 9 - 21 ［ DOI： 10.1016/j.jneumeth.2003.10.009 http://dx.doi.org/10.1016/j.jneumeth.2003.10.009 ］
Dimitrakopoulos G N ， Kakkos I ， Dai Z X ， Lim J ， DeSouza J J ， Bezerianos A and Sun Y . 2017 . Task-independent mental workload classification based upon common multiband EEG cortical connectivity . IEEE Transactions on Neural Systems and Rehabilitation Engineering ， 25 （ 11 ）： 1940 - 1949 ［ DOI： 10.1109/TNSRE.2017.2701002 http://dx.doi.org/10.1109/TNSRE.2017.2701002 ］
Du G L ， Su J S ， Zhang L L ， Su K ， Wang X Q ， Teng S H and Liu P X . 2022 . A multi-dimensional graph convolution network for EEG emotion recognition . IEEE Transactions on Instrumentation and Measurement ， 71 ： # 2518311 ［ DOI： 10.1109/TIM.2022.3204314 http://dx.doi.org/10.1109/TIM.2022.3204314 ］
Duanmu Z F ， Rehman A and Wang Z . 2018 . A quality-of-experience database for adaptive video streaming . IEEE Transactions on Broadcasting ， 64 （ 2 ）： 474 - 487 ［ DOI： 10.1109/TBC.2018.2822870 http://dx.doi.org/10.1109/TBC.2018.2822870 ］
Duanmu Z F ， Zeng K ， Ma K D ， Rehman A and Wang Z . 2017 . A quality-of-experience index for streaming video . IEEE Journal of Selected Topics in Signal Processing ， 11 （ 1 ）： 154 - 166 ［ DOI： 10.1109/JSTSP.2016.2608329 http://dx.doi.org/10.1109/JSTSP.2016.2608329 ］
Duanmu Z F ， Liu W T ， Li Z R ， Chen D Q ， Wang Z ， Wang Y Z and Gao W . 2020 . Assessing the quality-of-experience of adaptive bitrate video streaming ［EB/OL］. ［ 2023-07-21 ］. https://arxiv.org/pdf/2008.08804.pdf https://arxiv.org/pdf/2008.08804.pdf
Dwyer T . 2017 . Hecklevision， barrage cinema and bullet screens： an intercultural analysis . Journal of Audience and Reception Studies ， 2 （ 14 ）： 571 - 589
Engelke U ， Darcy D P ， Mulliken G H ， Bosse S ， Martini M G ， Arndt S ， Antons J N ， Chan K Y ， Ramzan N and Brunnström K . 2017 . Psychophysiology-based QoE assessment： a survey . IEEE Journal of Selected Topics in Signal Processing ， 11 （ 1 ）： 6 - 21 ［ DOI： 10.1109/JSTSP.2016.2609843 http://dx.doi.org/10.1109/JSTSP.2016.2609843 ］
Feng L ， Cheng C ， Zhao M Y ， Deng H Y and Zhang Y . 2022 . EEG-based emotion recognition using spatial-temporal graph convolutional LSTM with attention mechanism . IEEE Journal of Biomedical and Health Informatics ， 26 （ 11 ）： 5406 - 5417 ［ DOI： 10.1109/JBHI.2022.3198688 http://dx.doi.org/10.1109/JBHI.2022.3198688 ］
Friston K J . 2011 . Functional and effective connectivity： a review . Brain Connectivity ， 1 （ 1 ）： 13 - 36 ［ DOI： 10.1089/brain.2011.0008 http://dx.doi.org/10.1089/brain.2011.0008 ］
Harmon-Jones E and Gable P A . 2018 . On the role of asymmetric frontal cortical activity in approach and withdrawal motivation： an updated review of the evidence . Psychophysiology ， 55 （ 1 ）： # 12879 ［ DOI： 10.1111/psyp.12879 http://dx.doi.org/10.1111/psyp.12879 ］
Hernandez M T ， Sauerwein H C ， Jambaqué I ， De Guise E ， Lussier F ， Lortie A ， Dulac O and Lassonde M . 2003 . Attention， memory， and behavioral adjustment in children with frontal lobe epilepsy . Epilepsy and Behavior ， 4 （ 5 ）： 522 - 536 ［ DOI： 10.1016/j.yebeh.2003.07.014 http://dx.doi.org/10.1016/j.yebeh.2003.07.014 ］
Hjorth B . 1970 . EEG analysis based on time domain properties . Electroencephalography and Clinical Neurophysiology ， 29 （ 3 ）： 306 - 310 ［ DOI： 10.1016/0013-4694（70）90143-4 http://dx.doi.org/10.1016/0013-4694（70）90143-4 ］
Hong M and Kim G . 2020 . The effect of service quality of a ʻLive Streamingʼ dance performance on viewing satisfaction and on-site performance purchase intention . Korean Journal of Dance ， 20 （ 3 ）： 25 - 36
Hwang J ， Moon S E and Lee J S . 2018 . On the repeatability of EEG-based image quality assessment // Proceedings of 2018 IEEE International Conference on Systems， Man， and Cybernetics . Miyazaki， Japan ： IEEE： 1785 - 1788 ［ DOI： 10.1109/SMC.2018.00308 http://dx.doi.org/10.1109/SMC.2018.00308 ］
Koelstra S ， Muhl C ， Soleymani M ， Lee J S ， Yazdani A ， Ebrahimi T ， Pun T ， Nijholt A and Patras I . 2012 . DEAP： a database for emotion analysis； using physiological signals . IEEE Transactions on Affective Computing ， 3 （ 1 ）： 18 - 31 ［ DOI： 10.1109/T-AFFC.2011.15 http://dx.doi.org/10.1109/T-AFFC.2011.15 ］
Lawhern V J ， Solon A J ， Waytowich N R ， Gordon S M ， Hung C P and Lance B J . 2018 . EEGNet： a compact convolutional neural network for EEG-based brain-computer interfaces . Journal of Neural Engineering ， 15 （ 5 ）： # 056013 ［ DOI： 10.1088/1741-2552/aace8c http://dx.doi.org/10.1088/1741-2552/aace8c ］
Lee J ， Lee I and Kang J . 2019 . Self-attention graph pooling // Proceedings of the 36th International Conference on Machine Learning . Long Beach， USA ： PMLR： 3734 - 3743
Li J ， Krasula L ， Baveye Y ， Li Z and Le Callet P . 2019 . AccAnn： a new subjective assessment methodology for measuring acceptability and annoyance of quality of experience . IEEE Transactions on Multimedia ， 21 （ 10 ）： 2589 - 2602 ［ DOI： 10.1109/TMM.2019.2903722 http://dx.doi.org/10.1109/TMM.2019.2903722 ］
Li Y ， Liu Y ， Guo Y Z ， Liao X F ， Hu B and Yu T . 2022 . Spatio-temporal-spectral hierarchical graph convolutional network with semisupervised active learning for patient-specific seizure prediction . IEEE Transactions on Cybernetics ， 52 （ 11 ）： 12189 - 12204 ［ DOI： 10.1109/TCYB.2021.3071860 http://dx.doi.org/10.1109/TCYB.2021.3071860 ］
Li Z ， Geng B R ， Tao X M ， Duan Y P ， Gao D C and Hu S Z . 2021 . Video quality measurement for buffering time based on EEG frequency feature // Proceedings of 2021 IEEE International Conference on Image Processing . Anchorage， USA ： IEEE： 1424 - 1428 ［ DOI： 10.1109/ICIP42928.2021.9506401 http://dx.doi.org/10.1109/ICIP42928.2021.9506401 ］
Liu L Y and Wang J F . 2018 . Research on design of experience elements for bullet chatting video viewing . Industrial Design Research ， （ 1 ）： 107 - 110 .
刘灵豫 ， 王军锋 . 2018 . 弹幕视频观看体验要素设计研究 . 工业设计研究 ， （ 1 ）： 107 - 110
Liu X M ， Tao X M ， Xu M ， Zhan Y F and Lu J H . 2020 . An EEG-based study on perception of video distortion under various content motion conditions . IEEE Transactions on Multimedia ， 22 （ 4 ）： 949 - 960 ［ DOI： 10.1109/TMM.2019.2934425 http://dx.doi.org/10.1109/TMM.2019.2934425 ］
Luo J ， Wang Y J ， Liu G M ， Wang X F ， Lu X F and Hei X H . 2021 . Mirror convolutional neural network for motor imagery electroencephalogram recognition . Journal of Image and Graphics ， 26 （ 9 ）： 2257 - 2269
罗靖 ， 王耀杰 ， 刘光明 ， 王晓帆 ， 鲁晓锋 ， 黑新宏 . 2021 . 面向运动想象脑电图识别的镜卷积神经网络 . 中国图象图形学报 ， 26 （ 9 ）： 2257 - 2269 ［ DOI： 10.11834/jig.210072 http://dx.doi.org/10.11834/jig.210072 ］
Masruroh A H ， Imah E M and Rahmawati E . 2019 . Classification of emotional state based on EEG signal using AMGLVQ . Procedia Computer Science ， 157 ： 552 - 559 ［ DOI： 10.1016/j.procs.2019.09.013 http://dx.doi.org/10.1016/j.procs.2019.09.013 ］
Masson H L ， Pillet I ， Boets B and Op De Beeck H . 2020 . Task-dependent changes in functional connectivity during the observation of social and non-social touch interaction . Cortex ， 125 ： 73 - 89 ［ DOI： 10.1016/j.cortex.2019.12.011 http://dx.doi.org/10.1016/j.cortex.2019.12.011 ］
Mognon A ， Jovicich J ， Bruzzone L and Buiatti M . 2011 . Adjust： an automatic EEG artifact detector based on the joint use of spatial and temporal features . Psychophysiology ， 48 （ 2 ）： 229 - 240 ［ DOI： 10.1111/j.1469-8986.2010.01061.x http://dx.doi.org/10.1111/j.1469-8986.2010.01061.x ］
Reznik S J and Allen J J B . 2018 . Frontal asymmetry as a mediator and moderator of emotion： an updated review . Psychophysiology ， 55 （ 1 ）： # 12965 ［ DOI： 10.1111/psyp.12965 http://dx.doi.org/10.1111/psyp.12965 ］
Rolls E T ， Deco G ， Huang C C and Feng J F . 2022 . The human language effective connectome . NeuroImage ， 258 ： # 119352 ［ DOI： 10.1016/j.neuroimage.2022.119352 http://dx.doi.org/10.1016/j.neuroimage.2022.119352 ］
Rosenberg M D ， Finn E S ， Scheinost D ， Papademetris X ， Shen X L ， Constable R T and Chun M M . 2016 . A neuromarker of sustained attention from whole-brain functional connectivity . Nature Neuroscience ， 19 （ 1 ）： 165 - 171 ［ DOI： 10.1038/nn.4179 http://dx.doi.org/10.1038/nn.4179 ］
Ruan J J and Xie D L . 2021 . A survey on QoE-oriented VR video streaming： some research issues and challenges . Electronics ， 10 （ 17 ）： # 2155 ［ DOI： 10.3390/electronics10172155 http://dx.doi.org/10.3390/electronics10172155 ］
Saarimäki H ， Glerean E ， Smirnov D ， Mynttinen H ， Jääskeläinen I P ， Sams M and Nummenmaa L . 2022 . Classification of emotion categories based on functional connectivity patterns of the human brain . NeuroImage ， 247 ： # 118800 ［ DOI： 10.1016/j.neuroimage.2021.118800 http://dx.doi.org/10.1016/j.neuroimage.2021.118800 ］
Sakuma H ， Hori A ， Murashita M ， Kondo C and Hijikata Y . 2023 . YouTubers vs. VTubers： persuasiveness of human and virtual presenters in promotional videos . Frontiers in Computer Science ， 5 ： # 1043342 ［ DOI： 10.3389/fcomp.2023.1043342 http://dx.doi.org/10.3389/fcomp.2023.1043342 ］
Scholler S ， Bosse S ， Treder M S ， Blankertz B ， Curio G ， Müller K and Wiegand T . 2012 . Toward a direct measure of video quality perception using EEG . IEEE Transactions on Image Processing ， 21 （ 5 ）： 2619 - 2629 ［ DOI： 10.1109/TIP.2012.2187672 http://dx.doi.org/10.1109/TIP.2012.2187672 ］
Schwab J ， Bialow M ， Clemmons R ， Martin P and Holzer C . 1967 . The beck depression inventory with medical inpatients . Acta psychiatrica Scandinavica ， 43 （ 3 ）： 255 - 266 ［ DOI： 10.1111/j.1600-0447.1967.tb05762.x http://dx.doi.org/10.1111/j.1600-0447.1967.tb05762.x ］
Seubert J ， Humphreys G W ， Müller H J and Gramann K . 2008 . Straight after the turn： the role of the parietal lobes in egocentric space processing . Neurocase ， 14 （ 2 ）： 204 - 219 ［ DOI： 10.1080/13554790802108398 http://dx.doi.org/10.1080/13554790802108398 ］
Steer R A ， Ranieri W F ， Beck A T and Clark D A . 1993 . Further evidence for the validity of the beck anxiety inventory with psychiatric outpatients . Journal of Anxiety Disorders ， 7 （ 3 ）： 195 - 205 ［ DOI： 10.1016/0887-6185（93）90002-3 http://dx.doi.org/10.1016/0887-6185（93）90002-3 ］
Stuss D T and Alexander M P . 2005 . Does damage to the frontal lobes produce impairment in memory？ Current Directions in Psychological Science ， 14 （ 2 ）： 84 - 88 ［ DOI： 10.1111/j.0963-7214.2005.00340.x http://dx.doi.org/10.1111/j.0963-7214.2005.00340.x ］
Sudre G ， Szekely E ， Sharp W ， Kasparek S and Shaw P . 2017 . Multimodal mapping of the brain’s functional connectivity and the adult outcome of attention deficit hyperactivity disorder . Proceedings of the National Academy of Sciences of the United States of America ， 114 （ 44 ）： 11787 - 11792 ［ DOI： 10.1073/pnas.1705229114 http://dx.doi.org/10.1073/pnas.1705229114 ］
Sun X L ， Ma C ， Chen P Y ， Li M Y ， Wang H ， Dang W D ， Mu C X and Gao Z K . 2022 . A novel complex network-based graph convolutional network in major depressive disorder detection . IEEE Transactions on Instrumentation and Measurement ， 71 ： # 2519408 ［ DOI： 10.1109/TIM.2022.3211559 http://dx.doi.org/10.1109/TIM.2022.3211559 ］
Tao X M ， Du B and Duan Y P . 2021a . Key techniques of brain inspired video QoE prediction . ZTE Technology Journal ， 27 （ 1 ）： 33 - 36
陶晓明 ， 杜冰 ， 段一平 . 2021a . 脑启发视频用户体验评测关键技术 . 中兴通讯技术 ， 27 （ 1 ）： 33 - 36 .
Tao X M ， Yang Y ， Xu M ， Duan Y P ， Huang D L and Liu W Y . 2021b . Quality of experience oriented multimedia computing communications . Journal of Image and Graphics ， 26 （ 6 ）： 1201 - 1215
陶晓明 ， 杨铀 ， 徐迈 ， 段一平 ， 黄丹蓝 ， 刘文予 . 2021b . 面向体验质量的多媒体计算通信 . 中国图象图形学报 ， 26 （ 6 ）： 1201 - 1215 ［ DOI： 10.11834/jig.200864 http://dx.doi.org/10.11834/jig.200864 ］
Wagh N and Varatharajah Y . 2020 . EEG-GCNN： augmenting electroencephalogram-based neurological disease diagnosis using a domain-guided graph convolutional neural network // Proceedings of the Machine Learning for Health NeurIPS Workshop . Ithaca ： PMLR： 367 - 378
Watson D ， Clark L A and Tellegen A . 1988 . Development and validation of brief measures of positive and negative affect： the PANAS scales . Journal of Personality and Social Psychology ， 54 （ 6 ）： 1063 - 1070 ［ DOI： 10.1037//0022-3514.54.6.1063 http://dx.doi.org/10.1037//0022-3514.54.6.1063 ］
Wu X and Niu Y F . 2023 . A research on user’s QoE evaluation of Danmaku video based on EEG . Journal of Signal Processing ， 39 （ 8 ）： 1399 - 1407
邬霞 ， 牛一帆 . 2023 . 基于脑电的弹幕视频用户体验质量评估研究 . 信号处理 ， 39 （ 8 ）： 1399 - 1407 ［ DOI： 10.16798/j.issn.1003-0530.2023.08.006 http://dx.doi.org/10.16798/j.issn.1003-0530.2023.08.006 ］
Wu X ， Xu X Y ， Liu J H ， Wang H L ， Hu B and Nie F P . 2021 . Supervised feature selection with orthogonal regression and feature weighting . IEEE Transactions on Neural Networks and Learning Systems ， 32 （ 5 ）： 1831 - 1838 ［ DOI： 10.1109/TNNLS.2020.2991336 http://dx.doi.org/10.1109/TNNLS.2020.2991336 ］
Xie T X . 2018 . A Study on User Perceived Quality Evaluation of Danmaku Videos . Nanjing ： Nanjing University
谢添轩 . 2018 . 弹幕视频的用户感知质量评价研究 . 南京 ： 南京大学
Xu Z M ， Zhou Y Y ， Wen X Y ， Niu Y F ， Li Z Y ， Xu X J ， Zhang D Q and Wu X . 2022 . Cross subject personality assessment based on electroencephalogram functional connectivity and domain adaptation . Journal of Biomedical Engineering ， 39 （ 2 ）： 257 - 266
许子明 ， 周月莹 ， 温旭云 ， 牛一帆 ， 李子遇 ， 徐西嘉 ， 张道强 ， 邬霞 . 2022 . 基于脑电功能连接特征和领域自适应的跨被试人格评估 . 生物医学工程学杂志 ， 39 （ 2 ）： 257 - 266
Zheng W L and Lu B L . 2015 . Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks . IEEE Transactions on Autonomous Mental Development ， 7 （ 3 ）： 162 - 175 ［ DOI： 10.1109/TAMD.2015.2431497 http://dx.doi.org/10.1109/TAMD.2015.2431497 ］
Zhou Y Y ， Xu Z M ， Niu Y F ， Wang P P ， Wen X Y ， Wu X and Zhang D Q . 2022 . Cross-task cognitive workload recognition based on EEG and domain adaptation . IEEE Transactions on Neural Systems and Rehabilitation Engineering ， 30 ： 50 - 60 ［ DOI： 10.1109/TNSRE.2022.3140456 http://dx.doi.org/10.1109/TNSRE.2022.3140456 ］
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