基于AE和Transformer的运动想象脑电信号分类研究

蒋锐; 孙刘婷; 王小明; 李大鹏; 徐友云

doi:10.11959/j.issn.2096-3750.2023.00310

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基于AE和Transformer的运动想象脑电信号分类研究

理论与技术 | 更新时间：2024-08-16

- 基于AE和Transformer的运动想象脑电信号分类研究
- Research on EEG signal classification of motor imagery based on AE and Transformer
- 物联网学报 2023年7卷第1期页码：118-128
- 作者机构：
  
  1. 南京邮电大学通信与信息工程学院，江苏南京 210003
  2. 南京邮电大学通信与网络技术国家工程研究中心，江苏南京 210003
- 作者简介：
  
  [ "蒋锐（1985− ），男，博士，南京邮电大学副教授，主要研究方向为雷达信号处理、移动通信系统、脑波信号处理等" ]
  [ "孙刘婷（1997− ），女，南京邮电大学通信与信息工程学院硕士生，主要研究方向为脑波信号处理等" ]
  [ "王小明（1986− ），男，博士，南京邮电大学副教授，主要研究方向为通信资源协调分配、深度学习等" ]
  [ "李大鹏（1982− ），男，博士，南京邮电大学教授，主要研究方向为移动通信系统等" ]
  [ "徐友云（1966− ），男，博士，南京邮电大学教授，主要研究方向为移动通信系统、5G通信等" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61971241);国家自然科学基金资助项目;The National Natural Science Foundation of China(62071245)
- DOI：10.11959/j.issn.2096-3750.2023.00310
  中图分类号： TN911.73
- 纸质出版日期：2023-03-30，
  
  网络出版日期：2023-03，
- 稿件说明：
移动端阅览
蒋锐, 孙刘婷, 王小明, 等. 基于AE和Transformer的运动想象脑电信号分类研究[J]. 物联网学报, 2023,7(1):118-128.

RUI JIANG, LIUTING SUN, XIAOMING WANG, et al. Research on EEG signal classification of motor imagery based on AE and Transformer. [J]. Chinese journal on internet of things, 2023, 7(1): 118-128.
蒋锐, 孙刘婷, 王小明, 等. 基于AE和Transformer的运动想象脑电信号分类研究[J]. 物联网学报, 2023,7(1):118-128. DOI： 10.11959/j.issn.2096-3750.2023.00310.

RUI JIANG, LIUTING SUN, XIAOMING WANG, et al. Research on EEG signal classification of motor imagery based on AE and Transformer. [J]. Chinese journal on internet of things, 2023, 7(1): 118-128. DOI： 10.11959/j.issn.2096-3750.2023.00310.

摘要

基于运动想象的脑机接口系统一直是海内外研究学者的关注对象。针对传统运动想象脑电识别系统不能精准提取显著特征、分类识别准确率低等问题，提出一种新的基于自编码器（AE

auto-encoder）降维的 Transformer分类识别模型。该方法使用滤波器组共空间模式（FBCSP

filter bank common spatial pattern）对数据进行多个频段的特征提取，并利用AE获得降维后的特征矩阵。同时借助Transformer模型的位置编码考虑全局信号特征影响并利用多头自注意力机制考虑特征矩阵的内部关联性，提升系统分类识别效果。与传统的基于线性判别分析（LDA

linear discriminant analysis）的K-近邻（KNN

K-nearest neighbors）法分类识别系统进行对比，实验表明AE+Transformer模型的分类识别效果优于LDA+KNN系统，说明这种改进后的算法适用于运动想象的二分类。

Abstract

The motor imagery brain-computer interface has always been the focus of scholars.But traditional system cannot accurately extract significant signals and has low classification accuracy.To overcome such difficulty

a new Transformer model was proposed based on the auto-encoder (AE).The filter bank common spatial pattern (FBCSP) was used to extract the features of multiple frequency bands

and the AE was exploited to obtain the dimensionality-reduced feature matrix.Finally

it considered the influence of the global signal features by the position encoding of the Transformer model and considered the internal correlation of the feature matrix by using the multi-head self-attention mechanism.By comparison with the traditional K-nearest neighbors (KNN) system based on linear discriminant analysis (LDA)

the experimental results validates that the classification effect of AE+Transformer model is better than that of LDA+KNN system.It shows that the improved algorithm is suitable for the binary classification of motor imagery.

关键词

运动想象深度学习自编码器注意力模块Transformer模型

Keywords

motor imagerydeep learningauto-encoderattention moduleTransformer model

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