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  • ISSN 1006-3080
  • CN 31-1691/TQ

基于脑电功能连接微状态序列递归分析的情绪解码模型

李光强 陈宁 林家骏

李光强, 陈宁, 林家骏. 基于脑电功能连接微状态序列递归分析的情绪解码模型[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20211202003
引用本文: 李光强, 陈宁, 林家骏. 基于脑电功能连接微状态序列递归分析的情绪解码模型[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20211202003
LI Guangqiang, CHEN Ning, LIN Jiajun. Emotion Decoding Model Based on Recurrent Analysis of EEG Functional Connectivity Microstate Sequence[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20211202003
Citation: LI Guangqiang, CHEN Ning, LIN Jiajun. Emotion Decoding Model Based on Recurrent Analysis of EEG Functional Connectivity Microstate Sequence[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20211202003

基于脑电功能连接微状态序列递归分析的情绪解码模型

doi: 10.14135/j.cnki.1006-3080.20211202003
基金项目: 国家自然科学基金面上项目(61771196)基金项目:国家自然科学基金面上项目(61771196)
详细信息
    作者简介:

    李光强(1998-),男,山东人,硕士生,主要研究方向为音频和脑电信号的情感计算。E-mail:y30190694@mail.ecust.edu.cn

    通讯作者:

    陈 宁,E-mail: chenning_750210@163.com

  • 中图分类号: TP391

Emotion Decoding Model Based on Recurrent Analysis of EEG Functional Connectivity Microstate Sequence

  • 摘要: 脑电微状态代表准稳定的全局神经元活动,被认为是大脑动力学的构建模块,而基于微状态概率统计的特征不能很好表征脑电的动态变化特性。针对该问题提出了基于脑电微状态序列递归分析的情绪解码模型。首先,利用通道间的时域相关性建立每帧信号的微状态,并通过聚类获得微状态典型模式。然后,对典型模式时间序列构建递归图表征脑电动力学特性。最后,利用卷积神经网络对递归图进行情绪预测。在DEAP数据集上的实验结果表明该模型取得了比传统微状态方法更好的性能。

     

  • 图  1  基于脑电功能连接微状态序列递归分析的情绪解码模型

    Figure  1.  Emotion decoding model based on recurrent analysis of functional connectivity microstate sequence of EEG

    图  2  CNN模型结构

    Figure  2.  CNN model structure

    图  3  微状态典型模式的个数对模型性能的影响

    Figure  3.  The influence of the number of microstate typical patterns on model performance

    图  4  $ K{\text{ = }}5 $时基于微状态序列递归分析方法不同频带的性能对比

    Figure  4.  Performance comparison of different frequency bands based on microstate sequence recurrent analysis method when K=5

    图  5  $ K{\text{ = }}5 $时基于不同频带的微状态典型模式对比结果

    注:在每个子图中,显示了对应相关系数绝对值最大的 20%的边。红色和蓝色分别表示正关联和负关联,颜色的深浅反映关联权值绝对值的大小。圆的左右部分分别表示大脑的左右区域,左边的电极从上至下按照额区、中央区、颞区、顶区、枕区的位置排列,右边的电极排列相反。

    Figure  5.  Comparison results are based on typical microstate modes in different frequency bands when K=5

    表  1  与文献[9]的性能对比

    Table  1.   Performance comparison with [9]

    SubjectValenceArousal
    Ours[9]Ours[9]
    1 4.21 6.09 3.94 4.31
    2 7.41 7.94 6.40 8.33
    3 1.75 1.87 1.62 2.22
    4 4.96 4.54 3.04 3.59
    5 3.84 4.67 2.34 3.48
    6 1.56 1.99 1.77 1.90
    7 2.73 3.53 2.99 2.96
    8 2.90 3.81 1.77 1.98
    9 1.47 1.81 0.82 0.90
    10 3.29 3.33 1.69 1.94
    11 3.56 3.72 4.11 5.18
    12 3.96 4.03 1.88 2.82
    13 4.22 4.92 2.38 3.37
    14 4.05 4.02 1.80 2.86
    15 4.29 3.19 1.49 1.82
    16 2.44 3.07 2.49 3.19
    17 1.46 1.60 1.50 1.32
    18 1.22 1.32 1.31 1.71
    19 2.81 3.10 2.39 2.98
    20 2.38 2.64 1.24 1.44
    21 2.31 3.34 1.34 1.60
    22 5.71 5.40 2.99 3.28
    23 2.67 2.95 5.20 5.69
    24 3.43 4.08 1.49 2.02
    25 5.89 6.06 3.63 3.24
    26 4.78 6.72 4.34 5.53
    27 4.29 4.26 5.61 3.74
    28 4.21 6.26 4.71 6.18
    29 3.86 4.32 4.87 4.41
    30 1.73 1.72 1.65 1.56
    31 3.71 3.99 4.36 5.88
    32 3.30 3.50 2.12 2.45
    Mean 3.45 3.87 2.79 3.25
    Std 1.42 1.67 1.48 1.71
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-02
  • 网络出版日期:  2022-04-12

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