传感技术学报2024，Vol.37Issue(6)：997-1004,8.DOI:10.3969/j.issn.1004-1699.2024.06.009
一种基于静息态欧氏空间对齐迁移学习的运动想象源域样本筛选方法
A Source Domain Trial Selection Method for Motor Imagery Based on Resting-State Data Euclidean Space Alignment Transfer Learning
摘要
Abstract
To solve the problem of non-stationarity of EEG signal and complex and time-consuming calibration process,a source domain trial selection method based on Euclidean space rest data alignment is proposed.Firstly,the subjects'EEG trials are aligned with their rest-state data in Euclidean space to reduce the interference of factors unrelated to motor imagery task.Then,according to Euclidean distance measurement criteria,the source domain samples that are far away from the target domain samples are eliminated to further re-duce the difference between the source domain and the target domain samples,so as to improve the effect of transfer learning.The aver-age accuracy rates of 80.71%and 74.46%are obtained on two open motor imagery datasets of BCI Competition IV dataset1 and data-set2a,respectively.The experimental results show that the proposed method can effectively ameliorate the non-stationarity of EEG signal and improve the classification accuracy of motor imagery signals.关键词
脑机接口/运动想象/迁移学习/域适应/个体差异Key words
brain-computer interface/motor imagery/transfer learning/domain adaptation/individual difference分类
信息技术与安全科学引用本文复制引用
楚超,祝磊,杨君婷,黄爱爱,张建海..一种基于静息态欧氏空间对齐迁移学习的运动想象源域样本筛选方法[J].传感技术学报,2024,37(6):997-1004,8.基金项目
浙江省重点研发计划项目(2020C04009) （2020C04009）
