计算机应用与软件2024,Vol.41Issue(2) :80-85,177.DOI:10.3969/j.issn.1000-386x.2024.02.012

基于优化变分模态分解的脑电情绪识别

EMOTION RECOGNITION OF EEG BASED ON OPTIMAL VARIATIONAL MODE DECOMPOSITION

王雪蒙 郭滨 马欣
计算机应用与软件2024,Vol.41Issue(2) :80-85,177.DOI:10.3969/j.issn.1000-386x.2024.02.012

基于优化变分模态分解的脑电情绪识别

EMOTION RECOGNITION OF EEG BASED ON OPTIMAL VARIATIONAL MODE DECOMPOSITION

王雪蒙 1郭滨 1马欣1
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作者信息

  • 1. 长春理工大学电子信息工程学院 吉林 长春 130022
  • 折叠

摘要

为提高脑电情绪识别的准确性与可靠性,提出一种基于优化变分模态分解(VMD)的脑电情绪识别方法.对情绪脑电的节律信号VMD分解,引入磷虾群优化算法(KH)搜索VMD的最优分解层数和惩罚因子;从分解后的固有模态分量(IMFs)中提取平均能量、功率谱密度作为特征;利用XGBoost算法进行分类.实验结果表明,与EMD、EEMD等特征提取方法相比,该方法在DEAP数据集上达到了 91.02%的分类准确率,可以更有效地提取脑电情感特征,为脑电情绪识别的研究提供了新方法.

Abstract

In order to improve the accuracy and reliability of EEG emotion recognition,a recognition method of EGG emotion based on optimal variational mode decomposition(VMD)is proposed.The rhythm signal of emotional EEG was decomposed by VMD,and the krill swarm optimization(KH)was introduced to search the optimal decomposition layer number and punishment factor of VMD.The average energy and power spectral density were extracted from the decomposed intrinsic modal component(IMFs)as features.The XGBoost algorithm was used for classification.The experimental results show that compared with EMD and EEMD,the classification accuracy of this method in DEAP dataset reaches 91.02%,which can more effectively extract EEG emotional features,and provide a new method for the study of EEG emotion recognition.

关键词

脑电情绪识别/变分模态分解/磷虾群优化算法/固有模态分量

Key words

EEG emotion recognition/Variational mode decomposition/Krill swarm optimization/Inherent modal components

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基金项目

吉林省科技发展计划项目(20200404216YY)

出版年

2024
计算机应用与软件
上海市计算技术研究所 上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心
影响因子:0.615
ISSN:1000-386X
参考文献量7
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