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• 图像处理与计算机视觉 • 上一篇    下一篇

基于共同空间模式的情感脑电信号的空域特征提取

  

  1. (1. 安徽大学计算机科学与技术学院，安徽 合肥 230601；
    2. 杭州电子科技大学脑机协同智能技术浙江省重点实验室，浙江 杭州 310018)
  • 出版日期:2020-06-30 发布日期:2020-08-18
  • 基金资助:
    国家自然科学基金项目(61972437)；安徽省高等学校自然科学基金项目(KJ2018A0008)；脑机协同智能技术浙江省重点实验开放基金
    (BMCI2018-001)

Extraction of spatial features of emotional EEG signals based on common spatial pattern

  1. (1. School of Computer Science and Technology, Anhui University, Hefei Anhui 230601, China;
    2. Zhejiang Key Laboratory for Brain-Machine Collaborative Intelligence, Hangzhou Dianzi University, Hangzhou Zhejiang 310018, China)
  • Online:2020-06-30 Published:2020-08-18

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摘要： 为了改善基于脑电(EEG)的情感分类性能，提高多分类情况下的识别准确率，提出
了一种基于共同空间模式(CSP)的空域滤波算法。首先使用传统的CSP 方法设计空域滤波器，
并通过该滤波器对3 种情感类型(即积极、中性和消极)的EEG 信号进行线性投影，以提取空域
特征。此外，考虑到传统近似联合对角化(JAD)算法是使用“得分最高的特征值”准则进行特征向
量的选择，该情况可能导致无法有效区分多分类的情感状态，因此针对最高分特征值位置存在
的所有可能情况设计了不同的特征值选择方法。对实验室自主采集数据集，使用支持向量模型
(SVM)作为分类器进行对比实验。结果表明基于CSP 的空域特征提取方法在三分类情感识别中
平均准确率达到了87.54%，证明其在情感识别应用中具有可行性。

关键词: 情感脑-机交互, 共同空间模式, 近似联合对角化, 空域滤波, 情感识别

Abstract: In order to enhance the performance of electroencephalogram (EEG)-based emotion
recognition and improve the accuracy of multi-classification, a spatial filtering algorithm using the
common spatial pattern (CSP) was proposed. Firstly, the traditional CSP method was used to design
the spatial domain filter. On this basis, three types of emotion recognition EEG signals (i.e., positive,
neutral, and negative) were linearly projected by this filter, so as to extract spatial features.
Furthermore, considering that the traditional joint approximation diagonalization (JAD) algorithm
using the “highest score eigenvalue” criterion may result in the failure to distinguish the
multi-classification emotional states, different eigenvalue selection methods were designed in terms
of the position of the eigenvalues with the highest scores. Under our lab environment, the
comparative experiments using the support vector model (SVM) as a classifier have been carried out.
The results show that the CSP-based spatial feature extraction method has an impressive accuracy of 87.54% on average in three-class emotion state recognition, proving the feasibility of the method in  the application of emotion recognition.

Key words: affective-brain computer interaction, common spatial pattern, joint approximation diagonalization, spatial filtering, emotion recognition