作者
Ang Li, Zhenyu Wang, Xi Zhao, Tianheng Xu, Ting Zhou, Honglin Hu
发表日期
2023/3/20
期刊
IEEE Transactions on Neural Systems and Rehabilitation Engineering
卷号
31
页码范围
1743-1753
出版商
IEEE
简介
In recent years, deep neural network-based transfer learning (TL) has shown outstanding performance in EEG-based motor imagery (MI) brain-computer interface (BCI). However, due to the long preparation for pre-trained models and the arbitrariness of source domain selection, using deep transfer learning on different datasets and models is still challenging. In this paper, we proposed a multi-direction transfer learning (MDTL) strategy for cross-subject MI EEG-based BCI. This strategy utilizes data from multi-source domains to the target domain as well as from one multi-source domain to another multi-source domain. This strategy is model-independent so that it can be quickly deployed on existing models. Three generic deep learning models for MI classification (DeepConvNet, ShallowConvNet, and EEGNet) and two public motor imagery datasets (BCIC IV dataset 2a and Lee2019) are used in this study to verify …
学术搜索中的文章
A Li, Z Wang, X Zhao, T Xu, T Zhou, H Hu - IEEE Transactions on Neural Systems and …, 2023