计算机工程 ›› 2019, Vol. 45 ›› Issue (4): 296-301.doi: 10.19678/j.issn.1000-3428.0050948

• 开发研究与工程应用 • 上一篇    下一篇

基于数据空间自适应与共空间模式的脑电情感分类

陈景霞1,2,郑茹1,张鹏伟1,贾小云1   

  1. 1.陕西科技大学 电气与信息工程学院,西安 710021; 2.西北工业大学 计算机学院,西安 710129
  • 收稿日期:2018-03-26 出版日期:2019-04-15 发布日期:2019-04-15
  • 作者简介:陈景霞(1979—),女,副教授、硕士,主研方向为脑电信号处理、机器学习、模式识别;郑茹,硕士研究生;张鹏伟、贾小云,副教授、硕士。
  • 基金项目:

    国家自然科学基金青年基金(61806118)。

Electroencephalogram Emotion Classification Based on Data Space Adaptation and Common Spatial Pattern

CHEN Jingxia1,2,ZHENG Ru1,ZHANG Pengwei1,JIA Xiaoyun1   

  1. 1.College of Electrical and Information Engineering,Shaanxi University of Science and Technology,Xi’an 710021,China; 2.College of Computer Science,Northwestern Polytechnical University,Xi’an 710129,China
  • Received:2018-03-26 Online:2019-04-15 Published:2019-04-15

摘要:

为缓解日间脑电信号波动和差异导致情感分类性能下降的问题,在数据空间自适应(DSA)与共空间模式(CSP)迭代的基础上,提出一种用于脑电情感分类的特征提取算法。针对12个受试者连续5 d的情感脑电信号,采用DSA算法对脑电信号进行空间线性变换,再使用CSP将脑电信号变换到最优子空间,提取日间差异最小且类间差异最大的脑电功率谱密度特征及微分偏侧与差异因果特征。实验结果表明,该算法能提高脑电信号情感分类的准确率和稳定性。

关键词: 脑电, 数据空间自适应, 共空间模式, 迭代, 情感分类

Abstract:

In order to alleviate the problem of degraded emotional classification performance caused by fluctuation and difference of day-to-day Electroencephalogram(EEG) signals,an EEG feature extraction algorithm for EEG emotion classification based on the combination of Data Space Adaptation(DSA) and Common Spatial Pattern(CSP) iteration is proposed.For 5 days of EEG data from 12 subjects,the DSA algorithm is first used to perform spatial linear transformation to minimize the difference between cross-day EEG signals.Then,the CSP algorithm is used to transform it into an optimal subspace to maximize the variance between two emotional classes.The power spectral density features and differential lateral and causal features are extracted.Experimental results show that the proposed algorithm improves the accuracy and stability of emotional classification on EEG signals.

Key words: Electroencephalogram(EEG), Data Space Adaptation(DSA), Common Spatial Pattern(CSP), iteration, emotion classification

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