Journal of Guangdong University of Technology ›› 2021, Vol. 38 ›› Issue (04): 1-8.doi: 10.12052/gdutxb.210046

    Next Articles

Emotional Intelligence and Computational Psychophysiology

Hu Bin1,2, Zhou Ying-hui3, Tao Xiao-mei3,4   

  1. 1. School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China;
    2. Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
    3. School of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China;
    4. Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541004, China
  • Received:2021-03-18 Online:2021-07-10 Published:2021-05-25

Abstract: With the arrival of the third wave of development of Artificial Intelligence, Artificial Intelligence has entered a new stage, and the research of Emotional Intelligence is of great significance to the development of Artificial Intelligence. Emotional Intelligence has a wide range of application prospects in Intelligent Robots, Intelligent Virtual Assistants and other fields. Emotional Intelligence quantifies people's psychological state through Computational Psychophysiology, and Emotional Intelligence can enable machines to better recognize and understand human emotions, and produce better emotional interaction with users. The concepts of Emotional Intelligence and Computational Psychophysiology are expounded, the opportunities and key problems in the development of Emotional Intelligence analyzed, and the typical applications of Computational Psychophysiology in physiological signal acquisition, biological information feedback intervention and emotion recognition introduced based on EEG, speech, eye movement, expression and posture. Emotional Intelligence and Computational Psychophysiology have a high recognition rate in emotion recognition, which can use a variety of modals for emotion recognition, and have a wide range of development and application prospects in many fields such as medical treatment, education, entertainment, production and so on.

Key words: artificial intelligence, emotional intelligence, computational psychophysiology

CLC Number: 

  • R318
[1] HOWARD G. Frames of mind: the theory of multiple intelligences[M]. New York: Basic Books, 1983.
[2] SALOVEY P, MAYER J D. Emotional intelligence [J]. Imagination Cognition & Personality, 1990, 9(6): 217-236.
[3] PICARD R W. Affective computing[M]. Cambridge: MIT Press, 1995.
[4] FELDMAN B L, RUSSELL J A. Independence and bipolarity in the structure of current affect [J]. Journal of Personality and Social Psychology, 1998, 74(4): 967-984.
[5] EKMAN P. An argument for basic emotions [J]. Cognition & Emotion, 1992, 6(3-4): 169-200.
[6] MEHRABIAN A. Framework for a comprehensive description and measurement of emotional states [J]. Genetic, Social, and General Psychology Monographs, 1995, 121(3): 339-361.
[7] PLUTCHIK R. The emotions — facts, theories and a new model[M]. New York: Random House, 1962.
[8] Wikipedia. Emotion[EB/OL]. (2017-09-19)[2021-04-21]. https://en.wikipedia.org/wiki/Emotion.
[9] 国务院. 国务院关于印发新一代人工智能发展规划的通知[EB/OL]. (2017-07-20) [2021-01-05]. http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.
[10] STONE P, BROOKS R, BRYNJOLFSSON E, et al. Artificial intelligence and life in 2030: report of the 2015-2016 study panel[R]. Palo Alto: Stanford University, 2016.
[11] SINGH S. Global affective computing market - forecasts from 2017 to 2022[EB/OL]. (2017-01-12) [2021-01-05]. https://www.wiseguyreports.com/reports/874693-global-affective-computing-market-forecasts-from-2017-to-2022.
[12] HU B, FAN J. Computational psychophysiology [J]. Science(Supplement), 2015, 350(6256): 5.
[13] DONDERS F C. On the speed of mental processes [J]. Acta Psychologica, 1969, 30(1): 412-431.
[14] INSEL T R, CUTHBERT B N. Brain disorders? precisely [J]. Science, 2015, 348(6234): 499-500.
[15] 胡斌, 赵庆林, 彭宏. 一种便携式脑电采集方法: 201510511712.4[P]. 2016-09-21.
[16] PENG H, HU B, SHI Q, et al. Removal of ocular artifacts in EEG—an improved approach combining DWT and ANC for portable applications [J]. IEEE Journal of Biomedical and Health Informatics, 2013, 17(3): 600-607.
[17] CHEN M, MA Y, SONG J, et al. Smart clothing — connecting human with clouds and big data for sustainable health monitoring [J]. Mobile Networks and Applications, 2016, 21(5): 825-845.
[18] CAI H, HAN J, CHEN Y, et al. A pervasive approach to EEG-based depression detection [J]. Complexity, 2018, 2018(3): 1-13.
[19] CAI H, ZHANG X, ZHANG Y, et al. A case-based reasoning model for depression based on three-electrode EEG data [J]. IEEE Transactions on Affective Computing, 2018, 11(3): 383-392.
[20] 胡斌, 赵庆林, 彭宏, 等. 一种脑电与温度相结合的抑郁人群判定方法: 201610709400.9[P]. 2016-12-21.
[21] JIANG H, HU B, LIU Z, et al. Investigation of different speech types and emotions for detecting depression using different classifiers [J]. Speech Communication, 2017, 90(1): 39-46.
[22] LIU Z, KANG H, FENG L, et al. Speech pause time: a potential biomarker for depression detection[C]//2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Piscataway: IEEE, 2017: 2020-2025.
[23] 胡斌, 刘振宇, 康环宇. 基于语音特征与机器学习的抑郁症自动评估系统和方法: 201611147549.9[P]. 2017-05-31.
[24] LU S, XU J, LI M, et al. Attentional bias scores in patients with depression and effects of age — a controlled, eye-tracking study [J]. Journal of International Medical Research, 2017, 45(5): 1518-1527.
[25] LI M, LIU X, LU S, et al. A method of depression recognition based on visual information [J]. Journal of Medical Imaging and Health Informatics, 2017, 7(7): 1572-1579.
[26] 栗觅, 吕胜富, 王刚, 等. 一种情感带宽测定方法和系统: 201410440520.4[P]. 2014-11-19.
[27] LI M, LYU S, WANG G, et al. Affective bandwidth measurement and affective disorder determination: US9532711[P]. 2017-01-03.
[28] CAI B, XU X, GUO K, et al. A joint intrinsic-extrinsic prior model for retinex[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 4000-4009.
[29] LI J, LIU Z, DING Z, et al. A novel study for MDD detection through task-elicited facial cues[C]//2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Piscataway: IEEE, 2018: 1003-1008.
[30] HU X, CHENG J, ZHOU M, et al. Emotion-aware cognitive system in multi-channel cognitive radio ad hoc networks [J]. IEEE Communications Magazine, 2018, 56(4): 180-187.
[31] WANG T, LI C, WU C, et al. A gait assessment framework for depression detection using kinetic sensors [J]. IEEE Sensors Journal, 2020, 21(3): 3260-3270.
[32] CAI H, WANG Z, ZHANG Y, et al. A virtual-reality based neurofeedback game framework for depression rehabilitation using pervasive three-electrode EEG collector[C]//Proceedings of the 12th Chinese Conference on Computer Supported Cooperative Work and Social Computing. New York: ACM, 2017: 173-176.
[33] 胡斌, 蔡涵书, 赵庆林, 等. 一种基于虚拟现实的生物信息反馈系统: 201610868734.0[P]. 2017-02-08.
[1] Cui Tie-jun, Li Sha-sha. Realization of Intrinsic Safety in Production Process Based on Artificial Intelligence [J]. Journal of Guangdong University of Technology, 2021, 38(06): 84-90.
[2] Wang Pei-zhuang, Zeng Fan-hui, Sun Hui, Li Xing-sen, Guo Jian-wei, Meng Xiang-fu, He Jing. Extension of Knowledge Graph and its Intelligent Extension Library [J]. Journal of Guangdong University of Technology, 2021, 38(04): 9-16.
[3] Rao Dong-ning, Yang Jin-peng, Liu Yue-chang. A Survey of Temporal Planning [J]. Journal of Guangdong University of Technology, 2021, 38(03): 9-16.
[4] Cui Tie-jun, Li Sha-sha. Research on the Intelligent Science Theory of Oriental Thinking Based on Factor Driven [J]. Journal of Guangdong University of Technology, 2021, 38(01): 1-4.
[5] Qiu Ming-jin, Chen Jing-hua, Tang Jun-jie. An Overview of Optimal Power Flow with Wind Farm and Relevant Key Technology [J]. Journal of Guangdong University of Technology, 2018, 35(02): 63-68,94.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!