面向脑机接口基于改进的共同空间模式方法的单次运动想象脑电分类

唐智川; 李超; 吴剑锋; 刘鹏程; 程时伟

doi:10.1631/FITEE.1800083

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面向脑机接口基于改进的共同空间模式方法的单次运动想象脑电分类

常规文章 | Updated：2022-05-19

- 面向脑机接口基于改进的共同空间模式方法的单次运动想象脑电分类
- Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI
- 信息与电子工程前沿（英文） 2019年20卷第8期页码：1087-1098
- Affiliations：
  
  Industrial Design Institute, Zhejiang University of Technology, Hangzhou 310014, China
  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  Lincoln Centre for Autonomous Systems, University of Lincoln, Lincoln LN57DH, UK
  College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310014, China
- Author bio：
  
  [ "Zhi-chuan TANG, E-mail: ttzzcc@zjut.edu.cn" ]
  [ "Chao LI, E-mail:superli@zju.edu.cn" ]
  Jian-feng WU, E-mail:jianfw@zjut.edu.cn
  [ "Peng-cheng LIU, E-mail:pliu@cardiffmet.ac.uk" ]
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61702454 and 61772468), the MOE Project of Humanities and Social Sciences, China (No. 17YJC870018), the Fundamental Research Funds for the Provincial Universities of Zhejiang Province, China (No. GB201901006), and the Philosophy and Social Science Planning Fund Project of Zhejiang Province, China (No. 20NDQN260YB)
- DOI：10.1631/FITEE.1800083
  中图分类号： TP391.4
- 收稿：2018-02-01，
  
  修回：2019-;8-13，
  
  纸质出版：2019-08
- Accepted：
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唐智川, 李超, 吴剑锋, 等. 面向脑机接口基于改进的共同空间模式方法的单次运动想象脑电分类[J]. 信息与电子工程前沿（英文）, 2019,20(8):1087-1098.

Zhi-chuan TANG, Chao LI, Jian-feng WU, et al. Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1087-1098.
唐智川, 李超, 吴剑锋, 等. 面向脑机接口基于改进的共同空间模式方法的单次运动想象脑电分类[J]. 信息与电子工程前沿（英文）, 2019,20(8):1087-1098. DOI： 10.1631/FITEE.1800083.

Zhi-chuan TANG, Chao LI, Jian-feng WU, et al. Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1087-1098. DOI： 10.1631/FITEE.1800083.

摘要

单次运动想象脑电分类常用于脑机接口系统控制，是人-机之间的沟通桥梁。然而，脑电信号具有低信噪比和个性化差异，会对分类结果产生不利影响。本文提出一种改进的共同空间模式（B-CSP）方法，提取特征并消除负面影响。首先，针对不同被试，采用巴氏距离并基于事件相关去同步（ERD）和事件相关同步（ERS）模式选择每个电极通道的最优频率段；其次，采用B-CSP方法提取最优频率段脑电信号特征，获得可以最大程度区分两类运动想象的特征。采用所提方法对公共数据集和实验数据集提取特征，并结合反向传播神经网络进行单次运动想象脑电分类。将B-CSP方法与两种传统脑电特征提取方法—原始共同空间模式（CSP）和自回归（AR）—比较。采用B-CSP方法在公共数据集的表现（左手/双脚：91.25%±1.77%；左手/右手：84.50%±5.42%）和实验数据集的表现（左手/双脚：90.43%±4.26%）均优于两种传统方法。实验结果表明，本文所提方法能够有效分类运动想象脑电，并能对脑机接口系统开发提供实践和理论基础。

Abstract

Classifying single-trial electroencephalogram (EEG) based motor imagery (MI) tasks is extensively used to control brain-computer interface (BCI) applications

as a communication bridge between humans and computers. However

the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively. In this paper

we propose an improved common spatial pattern (B-CSP) method to extract features for alleviating these adverse effects. First

for different subjects

the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization (ERD) and event-related synchronization (ERS) patterns; then the signals of the optimal frequency band are decomposed into spatial patterns

and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data. The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network (BPNN) classifier to classify single-trial MI EEG. Another two conventional feature extraction methods

original common spatial pattern (CSP) and autoregressive (AR)

are used for comparison. An improved classification performance for both data sets (public data set: 91.25%±1.77% for left hand vs. foot and 84.50%±5.42% for left hand vs. right hand; experimental data set: 90.43%±4.26% for left hand vs. foot) verifies the advantages of the B-CSP method over conventional methods. The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively

and this study provides practical and theoretical approaches to BCI applications.

关键词

Keywords

references

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