电子学报2025，Vol.53Issue(8)：2830-2842,13.DOI:10.12263/DZXB.20250486
基于重构迁移子空间多视角领域适应的脑电情感分类方法
Reconstructed Transfer Subspace Based Multi-View Domain Adaptation Method for Electroencephalogram Emotion Classification
摘要
Abstract
Emotion recognition is the key link of intelligent human-computer interaction.Electroencephalogram(EEG)has become an important carrier of emotion analysis because it contains rich biological information and is difficult to disguise.However,EEG signal features are complex and changeable,and there are significant individual differences and time variability,which lead to low accuracy and poor generalization ability of traditional machine learning methods.To ad-dress these challenges,this paper proposes a reconstructed transfer subspace based multi view domain adaptation(RTS-MV-DA).This method regards different features as independent perspectives,explores the uniqueness and importance of each perspective through multi perspective learning,and mining their complementary relationship.Its core is to project the multi view data of the source domain and the target domain into a reconstruction migration subspace with low-rank constraints.In this subspace,RTS-MVDA,on the one hand,uses the reconstructed items to restore the original data information,and re-tains the main discrimination information through the low-rank representation;on the other hand,RTS-MVDA implements linear transformation to align the source domain and target domain,reducing the distribution difference between domains.In addition,RTS-MVDA constructs multi view supervised discriminant and global structure preserving item.The former us-es source domain label information to enhance intra class compactness and inter class separation,while the latter maintains the global structure distribution of data in the migration subspace,so as to more effectively migrate the discriminant knowl-edge of the source domain to the target domain.The experimental verification on the public database for emotion analysis using physiological signals(DEAP)dataset shows that the average accuracy of the proposed RTS-MVDA method in arousal and valence is 73.15%and 72.91%,respectively.Its precision,recall and F1-score are significantly better than the related comparison methods,effectively improving the accuracy and generalization ability of cross-subject EEG emotion recogni-tion.关键词
多视角学习/领域适应/子空间/低秩约束/脑电(EEG)/情感分类Key words
multi-view learning/domain adaptation/subspace/low-rank constraints/electroencephalogram(EEG)/emotion classification分类
信息技术与安全科学引用本文复制引用
韩少勇,周国华,殷新春..基于重构迁移子空间多视角领域适应的脑电情感分类方法[J].电子学报,2025,53(8):2830-2842,13.基金项目
国家自然科学基金(No.62032005) （No.62032005）
铜陵学院人才资助项目(No.2024tlxyrc020) （No.2024tlxyrc020）
铜陵学院科研项目(No.2025tlxyxdz051) National Natural Science Foundation of China(No.62032005) （No.2025tlxyxdz051）
The Talents Subsidized Project of Tongling University under Grant(No.2024tlxyrc020) （No.2024tlxyrc020）
The Research Project of Tongling University under Grant(No.2025tlxyxdz051) （No.2025tlxyxdz051）
