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基于相频特性的稳态视觉诱发电位深度学习分类模型

林艳飞 臧博宇 郭嵘骁 刘志文 高小榕

林艳飞, 臧博宇, 郭嵘骁, 刘志文, 高小榕. 基于相频特性的稳态视觉诱发电位深度学习分类模型[J]. 电子与信息学报, 2022, 44(2): 446-454. doi: 10.11999/JEIT210816
引用本文: 林艳飞, 臧博宇, 郭嵘骁, 刘志文, 高小榕. 基于相频特性的稳态视觉诱发电位深度学习分类模型[J]. 电子与信息学报, 2022, 44(2): 446-454. doi: 10.11999/JEIT210816
LIN Yanfei, ZANG Boyu, GUO Rongxiao, LIU Zhiwen, GAO Xiaorong. A Deep Learning Method for SSVEP Classification Based on Phase and Frequency Characteristics[J]. Journal of Electronics & Information Technology, 2022, 44(2): 446-454. doi: 10.11999/JEIT210816
Citation: LIN Yanfei, ZANG Boyu, GUO Rongxiao, LIU Zhiwen, GAO Xiaorong. A Deep Learning Method for SSVEP Classification Based on Phase and Frequency Characteristics[J]. Journal of Electronics & Information Technology, 2022, 44(2): 446-454. doi: 10.11999/JEIT210816

基于相频特性的稳态视觉诱发电位深度学习分类模型

doi: 10.11999/JEIT210816
基金项目: 国家自然科学基金(61601028, 61431007),北京市科技计划(Z201100004420015)
详细信息
    作者简介:

    林艳飞:女,1982年生,实验师,研究方向为脑电信号处理、生物医学信号处理

    臧博宇:男,1996年生,硕士,研究方向为脑电信号处理

    郭嵘骁:男,1998年生,硕士生,研究方向为脑机接口

    刘志文:男,1961年生,教授,博士生导师,研究方向为阵列信号处理、医学信号与图像处理、智能可穿戴医疗电子信息系统技术

    高小榕:男,1963年生,教授,博士生导师,研究方向为脑-机接口及神经工程学、生物医学信号处理

    通讯作者:

    林艳飞 linyf@bit.edu.cn

  • 中图分类号: TN911.7; TP391

A Deep Learning Method for SSVEP Classification Based on Phase and Frequency Characteristics

Funds: The National Natural Science Foundation of China (61601028, 61431007), Beijing Science and Technology Program (Z201100004420015)
  • 摘要: 针对现有深度学习分类方法对稳态视觉诱发电位相位与频率信息利用不充分的问题,该文提出一种用于稳态视觉诱发电位(SSVEP)分类的卷积神经网络模型。该模型以经过快速傅里叶变换后的复向量作为输入,首先对各个导联的实部向量和虚部向量进行卷积,学习相位信息;随后引入空间注意力机制,对判别频率信息进行增强;然后使用2维卷积和最大池化层进一步提取空域和频域信息;最后使用全连接层进行分类。实验结果表明利用该方法在跨受试情况下准确率可达到81.21%,通过在训练集增加标准正弦信号模板准确率可进一步提升至83.17%,相比典型相关分析方法获得了更好的分类效果。
  • 图  1  BETA公开SSVEP数据集刺激范式界面

    图  2  FFT处理示意图

    图  3  相位特征学习模块示意图

    图  4  频率增强(空间注意力)模块示意图

    图  5  PLFA-Net模型整体结构图

    图  6  CCA与PLFA-Net 5折交叉验证性能对比示意图

    图  7  CCA与PLFA-Net各频率相位SSVEP分类准确率对比示意图

    图  8  PLFA-Net与训练集数据增强的PLFA-Net 5折交叉验证性能对比图

    图  9  PLFA-Net与训练集数据增强的PLFA-Net各频率相位分类准确率对比示意图

    表  1  PLFA-Net模型详细结构及参数设置

    模块卷积核数量卷积核大小步长输出维度其他参数
    相位学习Input(60,188)
    Reshape(60,188,1)
    Conv2D4(2,1)(2,1)(30,188,4)max_norm=0.5
    BN(30,188,4)
    Activation(30,188,4)‘relu’
    频率特征增强MaxPooling2D (1,1)(1,1)(30,188,1)axis=–1
    AveragePooling2D(1,1)(1,1)(30,188,1)axis=–1
    Concatenate(30,188,2)axis=–1
    Conv2D1(5,5)(1,1)(30,188,1)padding=‘same’
    Activation(30,188,1)‘tanh’
    Multiply(30,188,4)
    Add(30,188,4)
    空频特征提取Conv2D4(5,5)(1,1)(26,184,4)max_norm=0.5
    BN(26,184,4)
    Activation(26,184,4)‘relu’
    MaxPooling2D(2,2)(13,92,4)
    SpatialDropout2D(13,92,4)p=0.5
    分类输出Flatten 4784
    Dense512512max_norm=0.5
    Activation512‘relu’
    Dense256256max_norm=0.5
    Activation256‘relu’
    Dense88max_norm=0.5
    Activation8‘softmax’
    下载: 导出CSV

    表  2  5折交叉验证PLFA-Net模型分类结果

    ACC (%)AUC
    1st80.020.9678
    2nd82.630.9754
    3rd79.480.9701
    4th82.630.9768
    5th81.300.9697
    平均81.21±0.810.9720±0.0034
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-11
  • 修回日期:  2022-01-17
  • 录用日期:  2022-01-20
  • 网络出版日期:  2022-01-21
  • 刊出日期:  2022-02-25

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