脑电情绪识别的深度学习研究综述
作者:
作者单位:

作者简介:

通讯作者:

马翠霞,E-mail:cuixia@iscas.ac.cn

基金项目:

北京市自然科学基金(4212029);国家自然科学基金(61872346);2019年牛顿奖中国奖(NP2PB/100047);中国博士后科学基金资助项目(2020M680697);江西省青年科学基金资助项目(20202BABL212006)


Deep Learning for EEG-based Emotion Recognition: A Survey
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
    摘要:

    情绪是情感的外在体现,影响人类的认知、感知、理性决策等日常活动.情绪识别作为实现计算机全面智能的一项基础任务,在情感计算和人机交互领域被深入研究和广泛应用.相比面部表情、语音或其他生理信号,利用脑电进行情绪识别具有时间分辨率高、成本低、识别效果好、可靠性高的优势.近年来,越来越多的深度学习框架被应用于基于脑电信号的情绪识别,并取得了比传统机器学习方法更加优异的效果.基于深度脑电特征的情绪识别是当前的研究热点之一,也具有一定的挑战性.目前,可供参考的针对此研究热点的综述文献较少.本文对近年来国内外相关文献进行调研分析,从模型输入、深度框架、实验设置、实验结果等方面对深度学习在基于脑电的情绪识别中的应用研究做了总结概况,并在DEAP和SEED这两个公开的脑电-情绪数据集上对具有代表性的方法进行了定性和定量的多方面对比,对这些方法存在的不足进行了分析和总结,同时也对未来可能的研究方向进行了展望.

    Abstract:

    Emotion is the external expression of affect, which has an influence on cognition, perception and decision-making of our daily life. As one of the basic problems in the realization of overall computer intelligence, emotion recognition has been studied in depth and widely applied in fields of affective computing and human-computer interaction. Comparing to facial expression, speech and other physiological signals, using EEG to recognize emotion is attracting more attention for its higher temporal resolution, lower cost, better identification accuracy and higher reliability. In recent years, more deep learning architectures are applied and have achieved better performance than traditional machine learning methods in this task. Deep learning for EEG-based emotion recognition is one of the research focuses and it remains many challenges to overcome. Considering that there exist few review literature to refer to, in this paper, we investigate the implementation of deep learning in EEG-based emotion recognition. Specifically, input formulation, deep learning architecture, experimental setting and results are surveyed. Besides, we carefully screen articles that evaluated their model on the widely used datasets, DEAP and SEED, perform qualitative and quantitative analysis from different aspects and make a comparison. Finally, we summarize the total work and give the prospect of future work.

    参考文献
    相似文献
    引证文献
引用本文

李锦瑶,杜肖兵,朱志亮,邓小明,马翠霞,王宏安.脑电情绪识别的深度学习研究综述.软件学报,,():0

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
历史
  • 收稿日期:2020-11-06
  • 最后修改日期:2021-05-09
  • 录用日期:
  • 在线发布日期: 2021-10-20
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京市海淀区中关村南四街4号,邮政编码:100190
电话:010-62562563 传真:010-62562533 Email:jos@iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号