Feature Extraction and Classification of EEG in Online Brain-Computer Interface

XU Bao-guo; SONG Ai-guo; FEI Shu-min

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Feature Extraction and Classification of EEG in Online Brain-Computer Interface

更新时间：2025-07-16

- Feature Extraction and Classification of EEG in Online Brain-Computer Interface
- Acta Electronica Sinica Vol. 39, Issue 5, Pages: 1025-1030(2011)
- 作者机构：
  
  1. 东南大学仪器科学与工程学院,江苏,南京,210096
  2. 东南大学自动化学院,江苏,南京,210096
  3. 东南大学仪器科学与工程学院江苏南京,210096
  4. 东南大学自动化学院江苏南京,210096
- 作者简介：
- 基金信息：
- DOI：
  CLC： TB99
- Published：2011
- 稿件说明：
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XU Bao-guo, SONG Ai-guo, FEI Shu-min. Feature Extraction and Classification of EEG in Online Brain-Computer Interface[J]. Acta Electronica Sinica, 2011, 39(5): 1025-1030.
DOI：

XU Bao-guo, SONG Ai-guo, FEI Shu-min. Feature Extraction and Classification of EEG in Online Brain-Computer Interface[J]. Acta Electronica Sinica, 2011, 39(5): 1025-1030. DOI：

摘要

在脑机接口研究中

针对运动想象脑电信号的特征抽取

提出了一种基于离散小波变换和AR模型的方法.利用Daubechies类小波函数对脑电信号进行3 层分解

抽取小波变换系数的统计特征;利用Burg算法提取脑电信号6阶AR模型系数.将这两类特征进行组合后使用神经网络、支持向量机、马氏距离线性判别进行分类并比较分析.采用BCI2003竞赛数据

以分类精度与竞赛的最好结果进行了比较

表明了所提出方法的有效性.在脑电信号控制机器人的在线系统中

该模式识别算法的平均准确度达到了89.5%.该特征提取和分类方法为在线脑机接口系统的研究提供了新的思路.

Abstract

In the study of brain-computer interface (BCI)

a novel method of extracting electroencephalography (EEG) features based on discrete wavelet transform (DWT) and autoregressive (AR) model was proposed.First

the EEG signal was decomposed to three levels by Daubechies wavelet function and statistics of wavelet coefficients were computed.Also

the sixth-order AR coefficients of the EEG signal were estimated using Burg's algorithm.Then

the combination features were used as an input vector for neural network (NN) classifier

support vector machine (SVM) classifier

and linear discriminant analysis (LDA) classifier.Performance of this feature extraction method was tested using the data set from BCI 2003 competition.The recognition rate was compared with the best result of the competition and the classification results showed the effectiveness of this algorithm.Moreover

applying this pattern recognition algorithm to online robot control system based on EEG

the average accuracy of 89.5% was obtained.This method provides a new idea for the study of online BCI system.

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