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基于RCNN-LSTM的脑电情感识别研究

柳长源 李文强 毕晓君

柳长源, 李文强, 毕晓君. 基于RCNN-LSTM的脑电情感识别研究. 自动化学报, 2020, 45(x): 1−9 doi: 10.16383/j.aas.c190357
引用本文: 柳长源, 李文强, 毕晓君. 基于RCNN-LSTM的脑电情感识别研究. 自动化学报, 2020, 45(x): 1−9 doi: 10.16383/j.aas.c190357
Liu Chang-Yuan, Li Wen-Qiang, Bi Xiao-Jun. Research on EEG emotion recognition based on RCNN-LSTM. Acta Automatica Sinica, 2020, 45(x): 1−9 doi: 10.16383/j.aas.c190357
Citation: Liu Chang-Yuan, Li Wen-Qiang, Bi Xiao-Jun. Research on EEG emotion recognition based on RCNN-LSTM. Acta Automatica Sinica, 2020, 45(x): 1−9 doi: 10.16383/j.aas.c190357

基于RCNN-LSTM的脑电情感识别研究

doi: 10.16383/j.aas.c190357
基金项目: 国家自然科学基金(5177090001)资助
详细信息
    作者简介:

    柳长源:哈尔滨理工大学电气与电子工程学院副教授. 主要研究方向为机器学习方法研究与改进、智能优化算法及脑电信号智能诊断技术.E-mail: liuchangyuan@hrbust.edu.cn

    李文强:哈尔滨理工大学电气与电子工程学院硕士研究生. 主要研究方向为深度学习与情感识别. 本文通信作者.E-mail: Lwqpost@163.com

    毕晓君:哈尔滨工程大学信息与通信工程学院教授. 中国人工智能学会自然计算专委会成员, 黑龙江省生物医学工程学会常务副理事长, 主要研究方向为信息智能处理技术、深度学习及智能优化算法.E-mail: bixiaojun@hrbeu.edu.cn

Research on EEG Emotion Recognition Based on RCNN-LSTM

Funds: National Natural Science Foundation of China (5177090001)
  • 摘要: 情感作为人脑的高级功能, 对人们的个性特征和心理健康有很大的影响, 利用网上公开的脑电情感数据库(Deap数据库), 根据心理效价和激励唤醒度等级进行情感划分, 对压力和平静等五种情感进行研究分析. 针对脑电信号时空特征结合的特点, 把深度学习中的卷积神经网络(Convolutional neural networks, CNN)和长短期记忆网络(Long short term memory, LSTM)两者作为基本前提, 并在此基础之上设计了一个RCNN-LSTM的脑电情感信号分类模型. 利用循环卷积神经网络(Recurrent convolutional neural network, RCNN)自动提取脑电信号中的抽象特征, 省去了人工选择与降维的过程, 然后结合LSTM网络对脑电情感信号进行分类识别. 实验结果表明, 利用该方法对5种情感类别的平均分类识别率达到了96.63%, 证明了该方法的有效性.
  • 图  1  一个含有内部环的RNN

    Fig.  1  An RNN with an inner ring

    图  2  RCNN结构分布图

    Fig.  2  RCNN structure distribution

    图  3  RCL展开图

    Fig.  3  RCL expansion diagram

    图  4  LSTM结构示意图

    Fig.  4  LSTM structure diagram

    图  5  RCNN-LSTM结构示意图

    Fig.  5  RNCN-LSTM structure diagram

    图  6  Ga-Svm的适应度曲线

    Fig.  6  Svm fitness curve

    表  1  RCNN网络模型结构

    Table  1  RCNN network model structure

    网络层 参数设置
    输入层 输入结构: (数据长度, 1)
    标准卷积层 核数量: 90; 大小: 3; 步长: 1
    最大池化层 核大小: 4 步长: 2
    RCL层 核数量: 90; 大小: 3; 步长: 1
    最大池化层 核大小: 4 步长: 2
    RCL层 核数量: 90; 大小: 3; 步长: 1
    最大池化层 核大小: 4 步长: 2
    RCL层 核数量: 90; 大小: 3; 步长: 1
    最大池化层 核大小: 4 步长: 2
    RCL层 核数量: 90; 大小: 3; 步长: 1
    最大池化层 核大小: 4 步长: 2
    下载: 导出CSV

    表  2  Deap数据收集统计表

    Table  2  Deap data collection statistics

    顺序 内容
    1 把电极放在适当的实验位置, 通过铃声来通知开始实验.
    2 接着调整基线约2 min, 直到符合要求(主要目的就是为了使受试人员能够保持放松)
    3 显示两秒的实验编号来提示具体的进程
    4 收集三秒钟的基线数据信息
    5 播放一分钟的音乐, 并且详细的记录这个时间段的数据信息
    6 对收集到的数据信息进行统计整理, 并且对各个相应的指标进行全面的评估
    下载: 导出CSV

    表  3  不同算法下的召回率和分类识别率(%)

    Table  3  Classification recognition rate under differrnt algorithms(%)

    轻松 沮丧 快乐 压力 平静 总体识别率
    RCNN+LSTM 100 96.6 95.7 96.5 94.3 96.63
    CNN+LSTM 79.2 77.1 86.1 92.0 85.4 83.83
    CNN 72.8 76.3 82.6 84.9 81.0 79.46
    LSTM 73.6 73.7 77.4 81.4 86.2 78.45
    SVM 68.0 75.4 83.4 75.2 77.2 75.92
    BP 76.0 72.8 75.6 84.1 74.7 76.59
    下载: 导出CSV

    表  4  不同算法下的kappa值和方差

    Table  4  Kappa values and variances under different algorithms

    kappa系数 方差
    RCNN+LSTM 0.928 0.04×10−2
    CNN+LSTM 0.763 0.06×10−2
    CNN 0.70 0.12×10−2
    LSTM 0.718 0.16×10−2
    SVM 0.658 0.27×10−2
    BP 0.675 0.16×10−2
    下载: 导出CSV

    表  5  已有研究成果与本文的对比

    Table  5  The existing research results are compared with this article.

    方法 分类类别 识别率
    SVM 2 70.1%
    KNN 2 77.8%
    CNN+RNN 2 73.09%
    SAE+RNN 4 79.26%
    DGCNN 3 90.4%
    3D-CNN 2 87.965%
    本文RCNN-LSTM 5 96.63%
    下载: 导出CSV
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  • 收稿日期:  2019-05-12
  • 录用日期:  2020-03-25

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