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基于混合自编码器的脑力负荷识别

周颖 陈兰岚

周颖, 陈兰岚. 基于混合自编码器的脑力负荷识别[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20210922003
引用本文: 周颖, 陈兰岚. 基于混合自编码器的脑力负荷识别[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20210922003
ZHOU Ying, CHEN Lanlan. Recognition of Mental Workload Based on Hybrid Autoencoders[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20210922003
Citation: ZHOU Ying, CHEN Lanlan. Recognition of Mental Workload Based on Hybrid Autoencoders[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20210922003

基于混合自编码器的脑力负荷识别

doi: 10.14135/j.cnki.1006-3080.20210922003
基金项目: 国家自然科学基金(61976091) ;中央高校基本科研业务费专项资金
详细信息
    作者简介:

    周颖:作者简介:周 颖(1996−),女,江苏无锡人,硕士生,主要研究方向为机器学习及其应用。E-mail:zhou1124zy@163.com

    通讯作者:

    陈兰岚,E-mail:llchen@ecust.edu.cn

  • 中图分类号: TP391

Recognition of Mental Workload Based on Hybrid Autoencoders

  • 摘要: 为了消除多通道近红外光谱信号中存在的冗余信息并提取抽象特征,构建了基于混合自编码器的脑力负荷识别模型。首先,将原始信号送入栈式自编码器中进行通道降维;然后使用卷积自编码器对降维后的信号进行无监督学习提取抽象特征,并将特征依次送入支持向量机、K 最近邻、随机森林3种基分类器中进行建模;最后用软、硬投票的集成策略来提高模型对脑力负荷识别的准确性。实验结果表明,混合自编码器具有良好的通道降维和提取抽象特征的能力,该模型在脑力负荷三分类任务中的准确率可以达到95.12%,相对于同类研究有明显提升。

     

  • 图  1  脑力负荷识别模型框架

    Figure  1.  Framework of mental workload estimation model

    图  2  栈式自编码器结构

    Figure  2.  Structure of stack autoencoder

    图  3  卷积自编码器结构

    Figure  3.  Structure of convolutional autoencoder

    图  4  实验流程

    Figure  4.  Experiment flow

    图  5  NIRS通道图

    Figure  5.  Channels of NIRS

    图  6  栈式自编码器的训练

    Figure  6.  Training process of stack autoencoder

    图  7  降维至不同通道数量后的分类精度对比

    Figure  7.  Comparison of classification accuracy under different channel numbers after dimensionality reduction

    图  8  不同通道降维方法的对比

    Figure  8.  Comparison of different channel dimensionality reduction methods

    图  9  卷积自编码器训练过程

    Figure  9.  Training process of convolutional autoencoder

    图  10  部分受试者的特征可视化

    Figure  10.  Visualization of several subjects

    表  1  卷积自编码器的超参数

    Table  1.   Hyperparameters of the convolutional autoencoder

    LayerTypeHyperparameters
    1Input-
    2Conv 1D16×5
    3MaxPool 1D2
    4Conv 1D64×5
    5BatchNormalization-
    6MaxPool 1D2
    7Conv 1D32×3
    8Conv 1D1×3
    9Conv 1D1×3
    10Conv 1D32×3
    11Up-Sampling2
    12Conv 1D64×5
    13Up-Sampling2
    14Conv 1D16×5
    15Flatten-
    16Dense-
    17Output-
    下载: 导出CSV

    表  2  不同模型的复杂度和精度对比

    Table  2.   Comparison of complexity and accuracy for different models

    FeatureChannel reductionAccuracy/%Training time/sTesting time/s
    Traditional features82.5937.981.20
    PCA84.8535.390.80
    SAE91.17124.391.12
    Deep features84.369.181.08
    PCA86.757.540.98
    SAE93.26118.530.95
    下载: 导出CSV

    表  3  基分类器的参数设置

    Table  3.   Parameter settings for base classifiers

    ClassiferParameter
    KNNk = 4
    SVMC = 8, gamma = 0.5, kernel = rbf
    RFn_estimators = 190, max_feature =12
    下载: 导出CSV

    表  4  基分类器的投票集成结果

    Table  4.   Voting integration results of base classifiers

    MethodAccuracy/%Recall/%Precision/%F1/%
    Mean(base classifiers)93.2693.4792.7892.45
    Voting(soft)95.1294.8795.3494.99
    Voting(hard)94.8793.9693.9593.87
    下载: 导出CSV

    表  5  同类研究的对比

    Table  5.   Comparison of similar studies

    MethodPhysiological signalCategoriesAccuracy/%
    Reference[21]NIRS276.9/76.0
    Reference[1]EEG287.61
    Reference[26]NIRS383.42
    Reference[27]EEG386.12
    Reference[28]NIRS485.90
    Reference[29]EEG、NIRS385.30
    Reference[30]EEG、NIRS396.20
    This paperNIRS395.1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-22
  • 录用日期:  2021-12-07
  • 网络出版日期:  2022-04-16

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