电子科技2026，Vol.39Issue(6)：12-24,13.DOI:10.16180/j.cnki.issn1007-7820.2026.06.002
基于脑电图的增强型图卷积网络架构的情感识别
Emotion Recognition Based on An Enhanced Graph Convolutional Network Architecture Using EEG
摘要
Abstract
In view of the affective computing problem of existing machine learning models in different electroen-cephalogram channels,different frequency bands within the same channel,and individual differences among different subjects,this study proposes an enhanced GCN(Graph Convolutional Network)based on EEG(Electroencephalo-gram)for affective recognition.The potential complementary information is explored through the multi-head attention mechanism,and the problem of insufficient processing of the importance differences of different electroencephalogram channels and frequency bands in existing emotion recognition models is solved by the fusion of the adjacency matrix and the extension matrix.The problems of differences in brain structure and activity patterns in cross-subject tasks are improved through adversarial training methods.The experimental results show that under the two paradigms of test-de-pendent and test-independent,the classification accuracies of the proposed method in the SEED-IV dataset are 84.46%and 75.92%respectively,and in the SEED-V dataset are 81.08%and 66.05%respectively.关键词
机器学习/域自适应/脑电图/情感识别/图卷积网络/多头注意力机制/再生域/对抗性训练Key words
machine learning/domain adaptation/EEG/emotion recognition/graph convolutional network/multihead attention mechanism/regeneration domain/antagonistic training分类
信息技术与安全科学引用本文复制引用
王雲生,吴文龙,尹钟,刘嘉豪..基于脑电图的增强型图卷积网络架构的情感识别[J].电子科技,2026,39(6):12-24,13.基金项目
国家自然科学基金(61703277) （61703277）
上海青年科技英才扬帆计划(17YF1427000)National Natural Science Foundation of China(61703277) （17YF1427000）
Shanghai Sailing Program(17YF1427000) （17YF1427000）
