计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (4): 760-774.DOI: 10.3778/j.issn.1673-9418.2107006
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
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。基金资助:
WANG Zhongmin1,2,3, ZHAO Yupeng1,+(), ZHENG Ronglin1, HE Yan1,2,3, ZHANG Jiawen1, LIU Yang1
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.Supported by:
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。
中图分类号:
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.
频带 | 描述 |
---|---|
| 极度疲劳和昏睡状态时,可在颞叶、顶叶记录到这种波段 |
| 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 |
| 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 |
| 精神紧张、焦虑、亢奋、注意力高度集中时会出现此波 |
| 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 |
---|---|
| 极度疲劳和昏睡状态时,可在颞叶、顶叶记录到这种波段 |
| 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 |
| 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 |
| 精神紧张、焦虑、亢奋、注意力高度集中时会出现此波 |
| 当出现多模态的感觉刺激会出现此波 |
作者 | 脑网络构建方法 | 度量指标 |
---|---|---|
Wang等人[ | 格兰杰因果关系 | — |
Ma等人[ | 相干性 | 聚类系数、特征路径长度 |
Chen等人[ | 格兰杰因果关系 | 连接密度、因果流 |
杨剑等人[ | 皮尔逊相关系数 | — |
Dasdemir等人[ | 相位锁值 | — |
Zhang等人[ | 相位锁值 | — |
Moon等人[ | 相位锁值、皮尔逊相关系数、相位滞后指数 | — |
Li等人[ | 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[ | 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[ | 相位锁值 | — |
Liu等人[ | 相位滞后指数 | 连接强度 |
Al-Shargie等人[ | 相位锁值 | — |
Deng等人[ | 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[ | 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[ | 格兰杰因果关系 | 介数 |
Sun等人[ | 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[ | 互信息 | 聚类系数、平均最短路径长度 |
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[ | 格兰杰因果关系 | — |
Ma等人[ | 相干性 | 聚类系数、特征路径长度 |
Chen等人[ | 格兰杰因果关系 | 连接密度、因果流 |
杨剑等人[ | 皮尔逊相关系数 | — |
Dasdemir等人[ | 相位锁值 | — |
Zhang等人[ | 相位锁值 | — |
Moon等人[ | 相位锁值、皮尔逊相关系数、相位滞后指数 | — |
Li等人[ | 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[ | 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[ | 相位锁值 | — |
Liu等人[ | 相位滞后指数 | 连接强度 |
Al-Shargie等人[ | 相位锁值 | — |
Deng等人[ | 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[ | 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[ | 格兰杰因果关系 | 介数 |
Sun等人[ | 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[ | 互信息 | 聚类系数、平均最短路径长度 |
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 |
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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