计算机应用与软件2024，Vol.41Issue(8)：92-100,125,10.DOI:10.3969/j.issn.1000-386x.2024.08.013
基于模型迁移的跨被试脑电情感分类算法
CROSS-SUBJECT EEG EMOTION CLASSIFICATION ALGORITHM BASED ON MODEL TRANSFER
摘要
Abstract
The distribution of EEG emotion signal varies greatly among different subjects,which leads to low classification recognition rate.Therefore,this paper proposes a cross-subject EEG sentiment classification algorithm based on model transfer.The initial model of the convolutional neural network was obtained by training the selected source domain data.The distributed similarity between the target domain and the source domain was maximized by iterative nearest point algorithm.A new convolutional neural network model was obtained by fine-tuning to identify the target domain data.The experimental results show that the proposed algorithm can realize the common use of the initial network model by different subjects,which greatly improves the efficiency of the model.The domain adaptation algorithm based on iterative nearest point makes the classification accuracy of EEG emotion model transfer over 90％,which provides a new idea for EEG emotion classification of different subjects.关键词
脑电情感信号/模型迁移/迭代最近点/域适应Key words
EEG emotion signal/Model transfer/Iterate the nearest point/Domain adaptive分类
信息技术与安全科学引用本文复制引用
韩劲,薄华,曹芳..基于模型迁移的跨被试脑电情感分类算法[J].计算机应用与软件,2024,41(8):92-100,125,10.基金项目
国家自然科学基金项目(61902239). （61902239）
