Unified Global Spatial Representation for EEG Subject-Independent Emotion Recognition

ZHANG Jing; WANG Yi-xin; REN Yong-gong

doi:10.12263/DZXB.20230016

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Unified Global Spatial Representation for EEG Subject-Independent Emotion Recognition

PAPERS | 更新时间：2025-12-08

- Unified Global Spatial Representation for EEG Subject-Independent Emotion Recognition
- ACTA ELECTRONICA SINICA Vol. 51, Issue 5, Pages: 1396-1404(2023)
- 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：
- 基金信息：
  
  National Natural Science Foundation of China(61902165;61976109);Liaoning Provincial Department of Science and Technology(2022JH2/101300271);Liaoning Provincial Department of Education(LJKMZ20221425)
- DOI：10.12263/DZXB.20230016
  CLC： TP391.4
- Received：03 January 2023，
  
  Revised：2023-03-15，
  
  Published：25 May 2023
- 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.

关键词

Keywords

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