基于小波包和串并行CNN的脑电信号分类

谷学静; 位占锋; 刘海望; 郭俊; 沈攀

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基于小波包和串并行CNN的脑电信号分类

更新时间：2026-09-04

- 基于小波包和串并行CNN的脑电信号分类
- EEG signal recognition based on wavelet packet and serial parallel CNN
- 微电子学与计算机 2021年38卷第6期页码：60-65
- 作者机构：
  
  1. 华北理工大学机构电气工程学院,河北,唐山,063210
  2. 唐山市数字媒体工程技术研究中心,河北,唐山,063000
  3. 华北理工大学轻工学院机构电气工程学院,河北,唐山,063000
- 作者简介：
- 基金信息：
  
  河北省自然科学基金项目 F2017209120
- DOI：
  中图分类号：
- 纸质出版：2021
- 稿件说明：
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谷学静, 位占锋, 刘海望, 郭俊, 沈攀. 基于小波包和串并行CNN的脑电信号分类[J]. 微电子学与计算机, 2021, 38(6): 60-65.

GU Xue-jing, WEI Zhan-feng, LIU Hai-wang, et al. EEG signal recognition based on wavelet packet and serial parallel CNN[J]. 2021, 38(6): 60-65.
谷学静, 位占锋, 刘海望, 郭俊, 沈攀. 基于小波包和串并行CNN的脑电信号分类[J]. 微电子学与计算机, 2021, 38(6): 60-65. DOI：

GU Xue-jing, WEI Zhan-feng, LIU Hai-wang, et al. EEG signal recognition based on wavelet packet and serial parallel CNN[J]. 2021, 38(6): 60-65. DOI：

摘要

针对运动想象脑电信号(EEG)的非线性、非平稳性特点，提出了一种结合小波包变换(WPT)和串并行卷积神经网络(SPCNN)的脑电信号分类方法.在小波包变换过程中，对脑电信号进行时频分解，选取与运动想象密切相关的频率段进行重构，重构后的脑电信号保留了有效的时频信息.考虑到脑电信号不同通道之间以及通道内的特征，构建了SPCNN网络模型自动提取有效的特征并进行分类.利用公开的竞赛数据集BCI competition Ⅳ 2b进行验证，结果表明：该方法能自适应的提取到有效特征，平均分类准确率达到了84.77%，比卷积神经网络提高了6.49%，为脑机接口系统的研究提供了一种分类方法.

Abstract

Aiming at the non-linear and non-stationary characteristics of motor imagery ElectroEncephaloGram (EEG) signals

a novel EEG signal classification method combining wavelet packet transform (WPT) and serial-parallel convolutional neural network(SPCNN) is proposed. In the process of wavelet packet transform

the EEG signal is decomposed in time and frequency

and the frequency band closely related to motor imagination is selected for reconstruction. The reconstructed EEG signal retains effective time-frequency information. Then

considering the features between and within the different channels of the EEG signal

the SPCNN network model is constructed to automatically extract the effective features and classify them. Use the public competition data set BCI competition Ⅳ 2b to verify

the results show that the method can adaptively extract effective features

and the average classification accuracy reaches 84.77%

which is 6.49% higher than the convolutional neural network. It provides a classification method for the research of brain-computer interface systems.

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references

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