Cross-subject emotional EEG recognition based on multi-source domain adaptation

Hanbing GAO; Chi ZHANG; Mingyan JIN; Yang XIAO; Fengyu CONG

doi:10.11959/j.issn.2096-6652.202106

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Cross-subject emotional EEG recognition based on multi-source domain adaptation

Special topic:emotional brain computer interface | 更新时间：2024-06-05

- Cross-subject emotional EEG recognition based on multi-source domain adaptation
- Chinese Journal of Intelligent Science and Technology Vol. 3, Issue 1, Pages: 59-64(2021)
- 作者机构：
  
  1. 大连理工大学电子信息与电气工程学部生物医学工程学院，辽宁大连 116024
  2. 大连理工大学电子信息与电气工程学部人工智能学院，辽宁大连 116024
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202106
  CLC： R318
- Published Online：2021-03，
  
  Published：15 March 2021
- 稿件说明：
移动端阅览
Hanbing GAO, Chi ZHANG, Mingyan JIN, et al. Cross-subject emotional EEG recognition based on multi-source domain adaptation[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 59-64.
DOI：

Hanbing GAO, Chi ZHANG, Mingyan JIN, et al. Cross-subject emotional EEG recognition based on multi-source domain adaptation[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 59-64. DOI： 10.11959/j.issn.2096-6652.202106.

摘要

在基于脑电信号的情感检测过程中，受被试间脑电信号的差异影响，传统的机器学习和深度学习方法不能很好地针对脑电数据建立通用的分类检测模型。针对此问题，将每个被试看作独立的域，建立针对脑电数据的多源域自适应的跨被试情感脑电识别模型，并在公开数据集上进行验证。验证结果表明，与单源域模型相比，提出的模型有更好的跨被试特征提取及分类能力。

Abstract

During the recognition of emotion based on electroencephalography (EEG) signals

traditional machine learning and deep learning methods cannot establish a general classification and detection model for EEG data due to the differences in EEG signals among subjects.Each subject was treated as an independent domain

a multi-source cross-subject emotional EEG recognition model was established

and the model was verified on the public dataset.The evaluation results show that compared with single-source domain model

the proposed model has better cross-subject feature extraction and classification capabilities.

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references

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