脑电信号情绪识别研究综述

doi:10.3778/j.issn.1673-9418.2107006

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (4): 760-774.DOI: 10.3778/j.issn.1673-9418.2107006

脑电信号情绪识别研究综述

王忠民¹^,²^,³, 赵玉鹏¹^,⁺(), 郑镕林¹, 贺炎¹^,²^,³, 张嘉雯¹, 刘洋¹

1.西安邮电大学计算机学院,西安 710121
2.陕西省网络数据分析与智能处理重点实验室,西安 710121
3.西安市大数据与智能计算重点实验室,西安 710121

收稿日期:2021-07-05 修回日期:2021-11-11 出版日期:2022-04-01 发布日期:2021-11-19
通讯作者: + E-mail: 1183708403@qq.com
作者简介:王忠民（1967—）,男,陕西蒲城人,博士,教授,硕士生导师,CCF高级会员,主要研究方向为嵌入式智能感知、脑机接口、机器学习、情感计算等。
赵玉鹏（1997—）,男,陕西渭南人,硕士研究生,主要研究方向为脑机接口技术、脑电情绪识别、脑电疲劳检测等。
郑镕林（1996—）,男,山西临汾人,硕士研究生,主要研究方向为脑机接口技术、脑电情绪识别、脑电疲劳检测。
贺炎（1980—）,女,湖南湘乡人,硕士,讲师,主要研究方向为大数据处理与应用、脑机接口。
张嘉雯（1997—）,女,陕西西安人,硕士研究生,主要研究方向为脑机接口技术、脑电情绪识别。
刘洋（1997—）,女,陕西延安人,硕士研究生,主要研究方向为脑机接口技术、脑电情绪识别。
基金资助:
国家自然科学基金(61373116);陕西省教育厅项目(18JK0697)

Survey of Research on EEG Signal Emotion Recognition

WANG Zhongmin¹^,²^,³, ZHAO Yupeng¹^,⁺(), ZHENG Ronglin¹, HE Yan¹^,²^,³, ZHANG Jiawen¹, LIU Yang¹

1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an 710121, China
3. Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an 710121, China

Received:2021-07-05 Revised:2021-11-11 Online:2022-04-01 Published:2021-11-19
About author:WANG Zhongmin, born in 1967, Ph.D., professor, M.S. supervisor, senior member of CCF. His research interests include embedded intelligent perception, brain-computer interface, machine learning, affective computing, etc.
ZHAO Yupeng, born in 1997, M.S. candidate. His research interests include brain-computer interface, EEG emotion recognition, EEG fatigue detection, etc.
ZHENG Ronglin, born in 1996, M.S. candidate. His research interests include brain-computer interface, EEG emotion recognition and EEG fatigue detection.
HE Yan, born in 1980, M.S., lecturer. Her research interests include big data processing and application and brain-computer interface.
ZHANG Jiawen, born in 1997, M.S. candidate. Her research interests include brain-computer interface and EEG emotion recognition.
LIU Yang, born in 1997, M.S. candidate. Her research interests include brain-computer interface and EEG emotion recognition.
Supported by:
National Natural Science Foundation of China(61373116);Project of Shaanxi Provincial Education Department(18JK0697)

摘要/Abstract

摘要：

情绪识别是指通过人的面部表情、行为动作或者生理信号等信息识别人的情绪状态,其成果在医疗辅助、教育、交通安全等方面有很大的应用价值。由于脑电信号的客观真实性等特点,使用脑电信号进行情绪识别研究受到国内外学者们的广泛关注。查阅了大量脑电情绪识别相关文献并进行归纳、分析和总结。首先,对情绪以及情绪识别的定义、情绪的分类模型、脑电信号的采集和预处理等理论知识进行了详细的解释和分析,给出了脑电情绪识别的一般框架。其次,从时域特征、频域特征、时频特征和非线性特征四方面综述了用于情绪识别的各类脑电特征的提取方法,介绍了脑功能网络的构建以及脑网络属性的提取方法,分析了每类特征和方法的优缺点。然后,对脑电情绪识别中常用的分类算法的特点、优缺点以及适用场景进行了分析。最后,对该领域目前的难点和未来的发展方向进行了总结和展望。可以帮助研究人员系统地了解基于脑电信号的情绪识别研究现状,为后续开展相关研究提供思路。

关键词: 情绪识别, 脑电信号, 特征提取, 情绪分类, 脑网络

Abstract:

Emotion recognition refers to the recognition of a person’s emotional state through information such as facial expressions, behavioral actions or physiological signals, and its results have great application value in medical assistance, education, traffic safety and other areas. Due to the objective and realistic characteristics of EEG signals, the research on emotion recognition using EEG signals has received a lot of attention from scholars at home and abroad. A large amount of literature related to EEG emotion recognition has been reviewed, analyzed and summarized. Firstly, the theoretical knowledge of emotions and the definition of emotion recognition, the classification model of emotions, the acquisition and pre-processing of EEG signals are explained and analyzed in detail, and a general framework of EEG emotion recognition is given. Secondly, the extraction methods of various types of EEG features used for emotion recognition are reviewed from four aspects: time domain features, frequency domain features, time-frequency features and non-linear features. The construction of brain functional networks and the extraction methods of brain network attributes are introduced, and the advantages and disadvantages of each type of features and methods are analyzed. Then, the characteristics, advantages and disadvantages as well as the applicable scenarios of the commonly used classification algorithms in EEG emotion recognition are analyzed. Finally, the current difficulties and future directions of the field are summarized and outlined. It can help researchers to systematically understand the current status of research on EEG signal-based emotion recognition and provide ideas for the subsequent development of related research.

Key words: emotion recognition, EEG signals, feature extraction, emotion classification, brain network

中图分类号:

TP183

王忠民, 赵玉鹏, 郑镕林, 贺炎, 张嘉雯, 刘洋. 脑电信号情绪识别研究综述[J]. 计算机科学与探索, 2022, 16(4): 760-774.

WANG Zhongmin, ZHAO Yupeng, ZHENG Ronglin, HE Yan, ZHANG Jiawen, LIU Yang. Survey of Research on EEG Signal Emotion Recognition[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(4): 760-774.

图/表 8

图1 二维情绪分类模型

Fig.1 Two-dimensional emotion classification model

表1 脑电信号的不同频带及其描述

Table 1 Different frequency bands of EEG signal and their description

频带	描述
$δ$ （0.5~4 Hz）	极度疲劳和昏睡状态时,可在颞叶、顶叶记录到这种波段
$θ$ （4~8 Hz）	在精神病患者中以及当成年人感到挫败时这种波最为明显
$α$ （8~12 Hz）	清醒、安静并闭眼时最为明显,它是正常人脑电波的基本节律
$β$ （12~30 Hz）	精神紧张、焦虑、亢奋、注意力高度集中时会出现此波
$γ$ （30~80 Hz）	当出现多模态的感觉刺激会出现此波

表1 脑电信号的不同频带及其描述

Table 1 Different frequency bands of EEG signal and their description

频带	描述
$δ$ （0.5~4 Hz）	极度疲劳和昏睡状态时,可在颞叶、顶叶记录到这种波段
$θ$ （4~8 Hz）	在精神病患者中以及当成年人感到挫败时这种波最为明显
$α$ （8~12 Hz）	清醒、安静并闭眼时最为明显,它是正常人脑电波的基本节律
$β$ （12~30 Hz）	精神紧张、焦虑、亢奋、注意力高度集中时会出现此波
$γ$ （30~80 Hz）	当出现多模态的感觉刺激会出现此波

图2 非侵入式脑电采集设备

Fig.2 Non-invasive EEG acquisition equipment

图3 国际标准10-20脑电记录系统电极安放位置

Fig.3 International standard 10-20 EEG recording system electrode placement

图4 脑电情绪识别框架

Fig.4 EEG emotion recognition framework

表2 基于脑网络的情绪识别研究

Table 2 Research on brain network-based emotion recognition

作者	脑网络构建方法	度量指标
Wang等人^[34]	格兰杰因果关系	—
Ma等人^[35]	相干性	聚类系数、特征路径长度
Chen等人^[36]	格兰杰因果关系	连接密度、因果流
杨剑等人^[37]	皮尔逊相关系数	—
Dasdemir等人^[38]	相位锁值	—
Zhang等人^[39]	相位锁值	—
Moon等人^[40]	相位锁值、皮尔逊相关系数、相位滞后指数	—
Li等人^[41]	相位锁值	聚类系数、最短路径长度、全局效率、局部效率
Wu等人^[42]	皮尔逊相关系数、谱相干	节点强度、聚类系数、特征向量中心度
Li等人^[43]	相位锁值	—
Liu等人^[44]	相位滞后指数	连接强度
Al-Shargie等人^[45]	相位锁值	—
Deng等人^[46]	相位锁值	网络均值、聚类系数、全局效率、局部效率、平均最短路径长度、介数中心度、节点度
Wang等人^[47]	相位锁值	特征路径长度、全局效率、聚类系数、传递性、局部效率
王斌等人^[48]	格兰杰因果关系	介数
Sun等人^[49]	相位锁值	聚类系数、平均最短路径长度、全局效率、局部效率、节点度、介数中心度
刘柯等人^[50]	互信息	聚类系数、平均最短路径长度

图5 面向情绪识别的脑网络分类

Fig.5 Brain network classification for emotion recognition

表3 常用脑网络构建方法

Table 3 Common brain network construction methods

脑网络类别	构建方法
功能性脑网络	相关分析、相干分析、互信息、似然同步、相位同步、相位滞后指数
因效性脑网络	格兰杰因果关系、部分定向相干

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脑电信号情绪识别研究综述

Survey of Research on EEG Signal Emotion Recognition

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