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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2024, Vol. 58 Issue (5): 879-890    DOI: 10.3785/j.issn.1008-973X.2024.05.001
    
Survey of deep learning based EEG data analysis technology
Bo ZHONG(),Pengfei WANG,Yiqiao WANG,Xiaoling WANG*()
School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
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Abstract  

A thorough analysis and cross-comparison of recent relevant works was provided, outlining a closed-loop process for EEG data analysis based on deep learning. EEG data were introduced, and the application of deep learning in three key stages: preprocessing, feature extraction, and model generalization was unfolded. The research ideas and solutions provided by deep learning algorithms in the respective stages were delineated, including the challenges and issues encountered at each stage. The main contributions and limitations of different algorithms were comprehensively summarized. The challenges faced and future directions of deep learning technology in handling EEG data at each stage were discussed.

Key words： electroencephalography (EEG)      closed-loop process      deep learning      preprocessing      feature extraction      model generalization     
Received: 23 October 2023      Published: 26 April 2024
CLC:  TP 392  
Fund:  国家自然科学基金资助项目（61972155）.
Corresponding Authors: Xiaoling WANG     E-mail: bzhong@stu.ecnu.edu.cn;xlwang@cs.ecnu.edu.cn
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Bo ZHONG
Pengfei WANG
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Cite this article:

Bo ZHONG,Pengfei WANG,Yiqiao WANG,Xiaoling WANG. Survey of deep learning based EEG data analysis technology. Journal of ZheJiang University (Engineering Science), 2024, 58(5): 879-890.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.05.001     OR     https://www.zjujournals.com/eng/Y2024/V58/I5/879


基于深度学习的EEG数据分析技术综述

对近年来的相关工作进行全面分析、横向比较，梳理出基于深度学习的EEG数据分析闭环流程. 对EEG数据进行介绍，从深度学习在EEG数据预处理、特征提取以及模型泛化3个关键阶段的应用进行展开，梳理深度学习算法在相应阶段提供的研究思路和解决方案，包括各阶段所存在的难点与问题. 全方位总结出不同算法的主要贡献和局限性，讨论深度学习技术在各个阶段处理EEG数据时所面临的挑战及未来的发展方向.


关键词： 头皮脑电(EEG),  闭环流程,  深度学习,  预处理,  特征提取,  模型泛化 
Fig.1 Closed-loop EEG analysis process
频段频率/Hz常见场景
δ<4成人慢波睡眠
θ4~7青少年或意志受挫的成人
α8~15放松、闭眼静息
β16~31警惕或激动
γ>32感知专注
μ8~12睡眠
Tab.1 Characteristics of EEG frequency bands
名称文献主要贡献网络架构
CNNLSTMGAN
EEGdenoiseNet文献[12]通过构造干净EEG和带有EOG和EMG伪影的数据集，以端到端的方式进行训练，
对EOG和EMG伪影进行去噪.		√√—
DenoisingEEG文献[13]
基于GAN，生成器从有噪声的EEG训练数据中进行采样去噪，并将其与
相应的干净EEG信号输入鉴别器中进行比较.		√—√
Auto-Denoising文献[14]
引入样本熵和基于能量阈值的数据归一化方法，将图像恢复的思想应用于脑电信号去噪，
使GAN模型中的生成器能够生成平稳的EEG信号.		—√√
U-NET文献[15]
将EEG信号转换为图像，使用计算机视觉领域分割经典模型U-NET.√——
EEGANet文献[16]
针对EOG伪影，不需要专家进行视觉检查或额外的EOG通道，可用于各种
条件下的EEG信号（无眼动、水平眼动、垂直眼动和眨眼）去伪迹.		√—√
Novel CNN文献[17]
引入多种网络模块，下采样EEG数据减少参数量、增加特征图数量、
提高网络深度，进而提高特征维度.		√——
1D-ResCNN文献[18]
将使用不同尺度卷积核的残差块组合起来，让模型捕捉更丰富的特征，
使得降噪后 EEG 的非线性特征得到显著保持，提高了在未知噪声下的 EEG 降噪性能.		√——
Tab.2 EEG signal denoising method based on deep learning
类型文献

主要贡献
数据增强文献[19]
利用额外的可变伸缩系数，动态调节WGAN-GP中的梯度惩罚项.
文献[20]
使用WGAN从低分辨率的记录中生成高空间分辨率EEG数据.
文献[21]
在WGAN的基础上，提出时空频率-均方误差损失，有助于重构出更具判别性的信号.
数据转换文献[22]
提出基于自编码器改进的深度学习模型，实现由EEG到同步SEEG信号的生成.
文献[23]
提出两阶段匹配策略，确定一对一电极匹配关系，基于CGAN实现EEG到SEEG的生成.
Tab.3 EEG signal generating method based on deep learning
文献主要贡献网络架构
LSTMCNNAEGAN
文献[24]
提取出脑电图信号分析中最常用的各种统计特征作为深度模型LSTM的输入，证明了LSTM是预测癫痫发作的理想工具.√———
文献[25]
在AE模型的基础上引入门控机制对输入向量进行部分遮蔽，促使编码层学习变量之间的关系，挖掘EEG序列内部不同变量间的内在关联，在重建恢复损坏的EEG数据方面具有优越的性能.——√—
文献[26]
通过CNN和AE的组合，捕获维度更低、信息量比例更大的EEG信号表示，适用于带宽有限、延时容忍度低的实时数据传输场景.—√√—
文献[27]
通过CNN提取EEG中蕴含的认知领域知识，对视觉刺激隐向量进行约束，在提高图像分类性能的同时，实现了由EEG图像认知特征生成对应的视觉图像的功能.—√—√
文献[28]
介绍并比较2种专门设计的自编码器学习EEG特征的策略，通道级自编码器专注于每个通道中的特征；图像级自编码器从整个片段中学习特征.—√√—
文献[29]
将EEG数据转化为一连串连续的保留拓扑结构的多光谱图像，保留EEG的空间、光谱和时间结构，从图像序列中学习信号的鲁棒表示，在心理负荷分类任务中表现
出优势.		√√——
Tab.4 Temporal feature extraction in EEG analysis based on deep learning
文献频域分解方式
特征提取/融合方式
文献[30]
提取$ \delta \mathrm{、}\theta \mathrm{、}\alpha \mathrm{、}\beta \mathrm{、}\gamma $波段将每个波段对应的特征向量直接连接
文献[31]
提取$ \delta \mathrm{、}\theta \mathrm{、}\alpha \mathrm{、}\beta $波段将多个子波段堆叠输入网络
文献[32]
提取$ \delta \mathrm{、}\theta \mathrm{、}\alpha \mathrm{、}\beta \mathrm{、}\gamma $波段，将相邻的波段结合形成不同尺度采用自适应的权重学习方法，对不同尺度的分类结果进行融合
文献[33]
提取$ \delta \mathrm{、}\theta \mathrm{、}\alpha \mathrm{、}\beta \mathrm{、}\gamma $波段，为每个受试者自适应地挑选最优波段将多个波段的输出直接连接
文献[34]
提取$ \theta \mathrm{、}\alpha \mathrm{、}\beta $波段将每个波段视为RGB中的一个通道，使用基于CNN的自编码器进行特征提取
文献[35]
将8~30 Hz分解至10个子波段，每个子波段宽为4 Hz，且相邻子波段之间的重叠频带为2 Hz将输入视为图像格式，沿着频率轴滑动卷积核
文献[36]
捕获频率≥2 Hz的信息为每个特定频率学习多个空间滤波器
文献[37]
—使用由小波核启发的时频滤波器
文献[38]
—将复杂网络表示扩展至频域，使用序列卷积从连续频率中学
习特征
Tab.5 Frequency feature extraction in EEG analysis based on deep learning
模型文献

主要贡献使用技术
注意力
机制		信道
建模		空间
信息		频域
信息		时域
信息
SPDNet RNN文献[39]
提出EEG协方差矩阵的时空融合方法，利用整个EEG信号的协方差矩阵提取时空特征，通过EEG通道之间的时空变化来研究驾驶员疲劳检测.——√—√
RACNN文献[40]
分别对不同脑功能区的频谱特征进行卷积计算学习EEG频谱-时空特征，利用注意力机制加权聚合各脑功能区特征，鼓励对最重要的区域赋予更大的注意力权重，揭示不同脑区EEG数据与任务存在的动态相关性.√——√√
RGNN文献[41]
考虑不同脑区之间的不同生物拓扑结构，捕捉EEG信道之间的局部和全局关系，采用符合生物原理的稀疏邻接矩阵捕捉局部和全局EEG通道间的关系. 实验结果表明，对于情感识别，前额叶、枕叶和顶叶可能是信息量最大的区域.—√———
IAG文献[44]
采用额外分支融合空间信息和频率信息，自适应表征不同EEG通道间的内在动态关系，为EEG情绪识别加入更多的特征.—√√√—
MD-AGCN文献[45]
纳入来自时间域、频域和脑功能连接的脑电图信息，将EEG的时域和频域与通道的拓扑结构相结合，以自适应的方式学习与情绪相关的大脑功能连接—√—√√
Tab.6 Spatial feature extraction in EEG analysis based on deep learning
训练目标文献
主要贡献使用技术
自监督
训练		域对抗
训练		数据
增强		多任务
学习		多视图
融合		知识
蒸馏
跨受试者文献[48]
构建片段级别的EEG特征序列，引入自监督任务，整个体系结构
都可以被微调到各种下游脑机接口和EEG分类任务.		√—————
跨受试者文献[50]
引入对抗性训练的方式，减少个体差异的影响，提取具有域
不变性的EEG信号特征.		—√————
跨任务文献[51]
通过聚合空间拼接、频率拼接多任务的学习，避免模型在单个
下游任务上的过拟合.		√——√——
跨视图文献[52]
提出EEG的多视图增强策略，在训练过程中利用多视图的互补信息.√—√—√—
跨模态文献[54]
采用跨模态的知识蒸馏策略，减小头皮EEG和基于耳朵EEG的
睡眠分级之间的性能差距.		—————√
跨数据集文献[55]
提出时频一致性策略，促使基于时间的表示和基于频率的
表示彼此接近.		√—√—√—
Tab.7 Deep learning based EEG model generalization methods
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