基于生理信号的情感计算研究综述

权学良; 曾志刚; 蒋建华; 张亚倩; 吕宝粮; 伍冬睿

doi:10.16383/j.aas.c200783

基于生理信号的情感计算研究综述

doi: 10.16383/j.aas.c200783

1.
华中科技大学人工智能与自动化学院图像信息处理与智能控制教育部重点实验室武汉 430074
2.
上海交通大学计算机科学与工程系上海 200240

基金项目: 湖北省技术创新专项资助项目(2019AEA171), 湖北省杰出青年基金(2020CFA050), 武汉市应用基础前沿项目(2020020601012240), 国家自然科学基金(61673266, 61976135)资助

详细信息

作者简介:
权学良：华中科技大学人工智能与自动化学院硕士研究生. 主要研究方向为机器学习, 脑机接口, 情感计算. E-mail: quanxl@hust.edu.cn

曾志刚：华中科技大学人工智能与自动化学院教授. 主要研究方向为神经网络理论与应用, 动力系统稳定性, 联想记忆. E-mail: zgzeng@mail.hust.edu.cn

蒋建华：华中科技大学人工智能与自动化学院副教授. 主要研究方向为燃料电池系统集成与控制, 动力电池系统管理, 系统优化. E-mail: jiangjh@hust.edu.cn

张亚倩：上海交通大学计算机科学与工程系研究助理教授. 主要研究方向为强化学习, 机器学习, 人机交互. E-mail: zhangyaqian@sjtu.edu.cn

吕宝粮：上海交通大学计算机科学与工程系教授. 主要研究方向为仿脑计算理论与模型, 神经网络, 机器学习, 脑−机交互, 情感计算. E-mail: blu@cs.sjtu.edu.cn

伍冬睿：华中科技大学人工智能与自动化学院教授. 主要研究方向为机器学习, 脑机接口, 计算智能, 情感计算. 本文通信作者. E-mail: drwu@hust.edu.cn

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出版历程
- 收稿日期: 2020-09-22
- 录用日期: 2020-12-31
- 网络出版日期: 2021-02-01
- 刊出日期: 2021-08-20

Physiological Signals Based Affective Computing: A Systematic Review

1.
Ministry of Education Key Laboratory on Image Information Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074
2.
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240

Funds: Supported by Technology Innovation Project of Hubei Province of China (2019AEA171), Hubei Province Distinguished Young Scholar Fund (2020CFA050), Wuhan Science and Technology Bureau (2020020601012240), and National Natural Science Foundation of China (61673266, 61976135)

More Information

Author Bio:
QUAN Xue-Liang　Master student at the school of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers machine learning, brain-computer interfaces, affective computing

ZENG Zhi-Gang　Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers neural networks, stability analysis of dynamic systems, associative memories

JIANG Jian-Hua　Associate professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers fuel cell system integration and control, power cell system management, system optimization

ZHANG Ya-Qian　Research assistant professor in the Department of Computer Science and Engineering, Shanghai Jiao Tong University. Her research interest covers reinforcement learning, machine learning, human-computer interaction

LV Bao-Liang　Professor in the Department of Computer Science and Engineering, Shanghai Jiao Tong University. His research interest covers brain-like computing, neural networks, machine learning, brain-computer interface, affective computing

WU Dong-Rui　Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers machine learning, brain-computer interfaces, computational intelligence, affective computing. Corresponding author of this paper

摘要

摘要:
情感计算是现代人机交互中的一个重要研究方向, 旨在研究与开发能够识别、解释、处理和模拟人类情感的理论、方法与系统. 脑电、心电、皮肤电等生理信号是情感计算中重要的输入信号. 本文总结了近年来基于脑电等生理信号的情感计算研究所取得的进展. 首先介绍情感计算的相关基础理论, 不同生理信号与情感变化之间的联系, 以及基于生理信号的情感计算工作流程和相关公开数据集. 接下来介绍生理信号的特征工程和情感计算中的机器学习算法, 重点介绍适合处理个体差异的迁移学习、降低数据标注量的主动学习和融合特征工程与学习器的深度学习算法. 最后, 指出基于生理信号的情感计算研究中面临的一些挑战.
- 情感计算 /
- 情绪分类 /
- 脑机接口 /
- 机器学习
Abstract:
Affective computing is an important research area in modern human-machine interaction. It aims to develop systems that can recognize, interpret, analyze and emulate human emotions. Physiological signals, e.g., electroencephalogram, electrocardiogram, galvanic skin response, etc., are important inputs in affective computing. This paper summarizes recent progresses on physiological signals based affective computing, particularly, electroencephalogram based affective computing. It first introduces the basic theories of affective computing, the relationship between the changes of physiological signals and affects, the flowchart of physiological signals based affective computing, and common public datasets. Next, it introduces typical feature engineering and machine learning algorithms, particularly transfer learning, active learning and deep learning. Finally, it points out some challenges and future research directions in physiological signals based affective computing.
- Affective computing /
- emotion classification /
- brain-computer interface /
- machine learning

HTML全文

图 1 情感计算研究发展简史

Fig. 1 A brief history of affective computing research

下载: 全尺寸图片幻灯片

图 2 情绪的连续型维度空间表示

Fig. 2 Continuous dimensional representations of emotions

下载: 全尺寸图片幻灯片

表 1 情感计算中常用的生理信号

Table 1 Common physiological signals in affective computing

生理信号类别	英文名称	英文缩写
脑电图	Electroencephalogram	EEG
肌电图	Electromyogram	EMG
心电图	Electrocardiogram	ECG
眼电图	Electrooculogram	EOG
心率变异性	Heart rate variability	HRV
皮肤电反应	Galvanic skin response	GSR
皮肤电应答	Electrodermal response	EDR
皮肤电活动	Electrodermal activity	EDA
血压信号	Blood pressure	BP
皮肤温度	Skin temperature	ST
呼吸模式	Respiration pattern	RSP
光电容积脉搏波	Photoplethysmogram	PPG
眼动信号	Eye movement	EM
脉搏信号	Pulse rate	PR
血氧饱和度	Oxygen saturation	SpO2

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表 2 脑电频率划分

Table 2 Frequency bands of EEG

脑波	频率	人体状态
$ \delta $	0.1 ~ 3 Hz	深度睡眠且没有做梦时
$ \theta $	4 ~ 7 Hz	成人情绪受到压力、失望或挫折时
$ \alpha $	8 ~ 12 Hz	放松、平静、闭眼但清醒时
$ \beta $	12.5 ~ 28 Hz	放松但精神集中、激动或焦虑
$ \gamma $	29 ~ 50 Hz	提高意识、幸福感、放松、冥想

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表 3 谷歌学术中2010年以来基于生理信号的情感计算工作统计

Table 3 Statistics of physiological signal based affective computing Google Scholar publications since 2010

生理信号类型	对应检索关键词	文献数量
脑电图	EEG OR Electroencephalogram	913
心电图	ECG OR Electrocardiogram	70
心率变异性	HRV OR (Heart rate variability)	38
皮肤电	GSR OR EDA OR EDR OR Electrodermal	27
肌电图	EMG OR Electromyogram	25
光电容积脉搏波	PPG OR Photoplethysmogram	13
血压	Blood pressure	7
脉搏	Pulse rate	4
皮肤温度	Skin temperature	3
眼电图	EOG OR Electrooculogram	2
血氧	SpO2 OR (Oxygen saturation) OR (Blood oxygen)	2
总计		1104
注: “情感计算” 对应的检索关键词为: (emotion OR emotional OR affect OR affective) + (recognize OR recognition OR classify OR classification OR detect OR detection OR predict OR prediction OR estimate OR estimation OR model OR state OR computing).

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表 4 部分最近的基于生理信号的情感计算工作

Table 4 Some recent studies on physiological signals based affective computing

参考文献	生理信号
[26]	GSR、PPG
[27]	EMG、GSR、PPG
[28]	EMG、GSR、BP
[29]	EEG、EMG、EOG、GSR、BP、ST、PR、EDA、RSP
[30]	EEG、ECG、GSR
[31]	EMG、ECG、EDR、BP、ST、RSP
[32]	ECG、EDA、ST
[33]	EEG、EM

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表 5 情感计算常用公开数据集

Table 5 Popular public affective computing datasets

数据集	内容说明	任务模型
MAHNOB-HCI^[34]	27 名被试的 EEG 及多种生理信号和图片、视频信息	VAD 模型
RECOLA^[35]	46 名被试的 ECG、EOG 及音频、视频信息	VA 模型
DECAF^[36]	30 名被试的 EOG、ECG、EMG 和视频信息	VA 模型
ASCERTAIN^[37]	58 名被试的 EEG、ECG、GSR 和图片信息	5 种情绪类别, VA 模型
AMIGOS^[38]	40 名被试的 EEG、ECG、GSR 及图片、视频信息	5 种情绪类别, VA 模型
DREAMER^[39]	23 名被试的 EEG、ECG	VAD 模型
RCLS^[40]	14 名被试的 EEG	3 种情绪类别
MPED^[41]	23 名被试的 EEG、ECG、RSP、GSR	7 种情绪类别
HR-EEG4EMO^[42]	27 名被试的 EEG	高兴、悲伤两种情绪类别
SEED^{[21, 33]}	15 名被试, 每名被试 3 次实验的 EEG	3 种情绪类别
SEED-IV^[33]	15 名被试, 每名被试 3 次实验的 EEG、EM	4 种情绪类别
DEAP^[43]	32 名被试的 EEG、EOG、EMG、GSR、RSP、BP、ST	VAD 模型

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表 6 不同深度特征提取方式及效果

Table 6 Different deep learning methods of feature extract and their effects

作者及参考文献	神经网络模型	数据集	准确率
Yin等^[109]	堆叠式自编码器	DEAP	83.0 % (Valence/2)、84.1 % (Arousal/2)
Fourati等^[110]	回声状态网络	DEAP	71.0 % (Valence/2)、68.3 % (Arousal/2)
Ren等^[111]	融合大脑不对称特性的回声状态网络	DEAP	78.2 % (Average/4)
Liu等^[112]	多层次特征引导胶囊网络	DEAP	98.0 % (Valence/2)、98.3 % (Arousal/2)、98.3 % (Dominance/2)
Wu等^[113]	关键子网络选择	SEED	81.5 % (Average/3)
Yang等^[114]	具有子网节点的分层网络模型	SEED	85.7 % (Average/3)
Wang等^[115]	双向长短期记忆网络	SEED	95.0 % (Average/3)
Zhang等^[116]	变分路径推理	SEED	94.3 % (Average/3)
Cimtay等^[117]	卷积神经网络	SEED	73.7 % (Average/3)、82.9 % (Average/2)
注: (Valence/2)表示Valence维度2分类准确率, (Average/4)表示情绪4分类准确率.

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