Emotional Intelligence and Computational Psychophysiology

Hu Bin; Zhou Ying-hui; Tao Xiao-mei

doi:10.12052/gdutxb.210046

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Emotional Intelligence and Computational Psychophysiology

更新时间：2026-05-27

- Emotional Intelligence and Computational Psychophysiology
- Vol. 38, Issue 4, Pages: 1-8(2021)
- 作者机构：
  
  1. 兰州大学信息科学与工程学院,甘肃,兰州,730000
  2. 兰州大学甘肃省可穿戴装备重点实验室,甘肃,兰州,730000
  3. 桂林理工大学信息科学与工程学院,广西,桂林,541004
  4. 桂林理工大学广西嵌入式技术与智能系统重点实验室,广西,桂林,541004
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.210046
  CLC： R318;TP182
- Online First：10 July 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
胡斌, 周颖慧, 陶小梅. 情感智能与心理生理计算[J]. 广东工业大学学报, 2021, 38(4): 1-8.

Hu Bin, Zhou Ying-hui, Tao Xiao-mei. Emotional Intelligence and Computational Psychophysiology[J]. 2021, 38(4): 1-8.
胡斌, 周颖慧, 陶小梅. 情感智能与心理生理计算[J]. 广东工业大学学报, 2021, 38(4): 1-8. DOI： 10.12052/gdutxb.210046.

Hu Bin, Zhou Ying-hui, Tao Xiao-mei. Emotional Intelligence and Computational Psychophysiology[J]. 2021, 38(4): 1-8. DOI： 10.12052/gdutxb.210046.

摘要

随着人工智能第三次发展浪潮的到来

人工智能进入了新阶段

情感智能的研究对于人工智能的发展有着重大意义。情感智能在智能机器人、智能虚拟助手等领域具有广泛的应用前景

通过心理生理计算来量化感知人的心理状态

可赋予机器更好地识别、理解人类情感的能力

并与用户产生更好的感性化交互。本文阐述了情感智能和心理生理计算的概念

分析了目前情感智能发展的机遇与关键问题

介绍了心理生理计算在生理信号获取、生物信息反馈干预和基于脑电、语音、眼动、表情、姿态的情感识别等方面的典型应用。情感智能与心理生理计算在情感识别上有较高的识别率

能够利用多种模态进行情感识别

在医疗、教育、娱乐、生产等多个领域都有广泛的发展与应用前景。

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.

关键词

Keywords

references

HOWARD G. Frames of mind: the theory of multiple intelligences[M]. New York: Basic Books, 1983.

SALOVEY P, MAYER J D. Emotional intelligence [J]. Imagination Cognition Personality, 1990, 9(6): 217-236.

PICARD R W. Affective computing[M]. Cambridge: MIT Press, 1995.

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.

EKMAN P. An argument for basic emotions [J]. Cognition Emotion, 1992, 6(3-4): 169-200.

MEHRABIAN A. Framework for a comprehensive description and measurement of emotional states [J]. Genetic, Social, and General Psychology Monographs, 1995, 121(3): 339-361.

PLUTCHIK R. The emotions — facts, theories and a new model[M]. New York: Random House, 1962.

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.

HU B, FAN J. Computational psychophysiology [J]. Science(Supplement), 2015, 350(6256): 5.

DONDERS F C. On the speed of mental processes [J]. Acta Psychologica, 1969, 30(1): 412-431.

INSEL T R, CUTHBERT B N. Brain disorders? precisely [J]. Science, 2015, 348(6234): 499-500.

胡斌, 赵庆林, 彭宏. 一种便携式脑电采集方法: 201510511712.4[P]. 2016-09-21.

PENG H, HU B, SHI Q, et al . Removal of ocular artifacts in EEG—an improved approach combining DWT and ANC for portable applicat ions [J ] . IEEE Journal of Biomedical and Health Informatics, 2013, 17(3): 600-607.

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.

CAI H, HAN J, CHEN Y, et al . A pervasive approach to EEG-based depression detection [J ] . Complexity, 2018, 2018(3): 1-13.

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.

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.

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.

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.

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.

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.

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.

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.

LI M, LYU S, WANG G, et al. Affective bandwidth measurement and affective disorder determination: US9532711[P]. 2017-01-03.

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.

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.

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.

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.

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.

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.

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