统一全局空间表达的脑电信号跨被试情感识别

张晶; 王翌歆; 任永功

doi:10.12263/DZXB.20230016

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统一全局空间表达的脑电信号跨被试情感识别

学术论文 | 更新时间：2025-12-08

- 统一全局空间表达的脑电信号跨被试情感识别
- Unified Global Spatial Representation for EEG Subject-Independent Emotion Recognition
- 电子学报 2023年51卷第5期页码：1396-1404
- 作者机构：
  
  辽宁师范大学计算机与信息技术学院，辽宁大连 116081
- 作者简介：
  
  [ "张晶女，1984年出生于黑龙江省哈尔滨市.现为辽宁师范大学计算机与信息技术学院副教授. E-mail: zhangjing_0412@lnnu.edu.cn" ]
  [ "王翌歆男，1995年出生于河南省周口市.现于辽宁师范大学计算机与信息技术学研究生在读. E-mail: wangyixin277@163.com" ]
  [ "任永功（通讯作者）男，1972年出生于辽宁省大连市. 现为辽宁师范大学计算机与信息技术学院教授." ]
- 基金信息：
  
  国家自然科学基金项目(61902165;61976109);辽宁省科技厅重点研发项目(2022JH2/101300271);辽宁省教育厅高校基本科研项目(LJKMZ20221425)
- DOI：10.12263/DZXB.20230016
  中图分类号： TP391.4
- 收稿：2023-01-03，
  
  修回：2023-03-15，
  
  纸质出版：2023-05-25
- 稿件说明：
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张晶,王翌歆,任永功.统一全局空间表达的脑电信号跨被试情感识别[J].电子学报,2023,51(05):1396-1404.

ZHANG Jing,WANG Yi-xin,REN Yong-gong.Unified Global Spatial Representation for EEG Subject-Independent Emotion Recognition[J].ACTA ELECTRONICA SINICA,2023,51(05):1396-1404.
张晶,王翌歆,任永功.统一全局空间表达的脑电信号跨被试情感识别[J].电子学报,2023,51(05):1396-1404. DOI： 10.12263/DZXB.20230016.

ZHANG Jing,WANG Yi-xin,REN Yong-gong.Unified Global Spatial Representation for EEG Subject-Independent Emotion Recognition[J].ACTA ELECTRONICA SINICA,2023,51(05):1396-1404. DOI： 10.12263/DZXB.20230016.

摘要

脑电信号（ElectroEncephaloGram， EEG）的跨被试情感识别，充分利用EEG信号库中大规模信息，并避免单被试模型训练对被试数据过度依赖产生的模型失效等问题，进一步推广了脑电识别的广泛应用.然而，不同被试生理与心理等客观差异加剧了模型建立难度.基于此，本文提出统一全局空间表达（Unified Global Spatial Representation， UGSR）的跨被试识别模型.本文构建自适应在线自编码网络，通过对时序数据增量学习，提取EEG信号潜在统一特征，实现生理偏差校正.进一步，本文利用格拉姆角场（Gramian Angular Fields， GAF）转换局部时序特征为全局连续空间表达，避免相同环境下因被试心理差异产生反应信号时序不一致等问题，并建立全局注意力机制的深度卷积神经网络，获得更具判别性的非线性样本表达，提升识别精度.本文模型被验证在流行的脑电信号数据集上，并获得了更好的跨被试识别精度与泛化性.

Abstract

Electroencephalogram (EEG) subject-independent emotion recognition fully utilizes the built database of EEG

and avoids models of depending so heavily on training subjects. However

subject-independent emotion recognition suffers from the fairly low accuracy and generalization due to subjects born with individual difference in physical and psychological. To address above challenges

this paper proposes the unified global spatial representation model (UGSR). This paper presents self-adaption incremental auto-encoder network to obtain the latent unified features of all subjects without ground-truth to correct errors originating from physiological difference. Furthermore

this paper utilizes the gramian angular fields (GAF) to transfer from local time-features to global spatial-features dealing with the semantic invalidation

moreover

exploits attention-CNN (Convolutional Neural Network) with the non-linear representation ability to extract the discriminate representation. The proposed model is verified in popular datasets

and achieves better performances than state-of-the-art methods.

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Keywords

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