文献类型：期刊文献

英文题名：A novel dual-step transfer framework based on domain selection and feature alignment for motor imagery decoding

作者：Bai, Guanglian[1];Jin, Jing[1,2];Xu, Ren[3];Wang, Xingyu[1];Cichocki, Andrzej[4,5]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Shenzhen Res Inst, Shenzhen 518063, Peoples R China;[3]Guger Technol OG, Graz, Austria;[4]Polish Acad Sci, Syst Res Inst, Warsaw, Poland;[5]Nicolaus Copernicus Univ, Dept Informat, Torun, Poland

年份：2024

卷号：18

期号：6

起止页码：3549

外文期刊名：COGNITIVE NEURODYNAMICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001234026500001)】；

基金：This work was supported by STI 2030-major projects 2022ZD0208900 and the Grant National Natural Science Foundation of China under Grant 62176090; in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZXin part by the Program of Introducing Talents of Discipline to Universities through the 111 Project under Grant B17017; This research is also supported by National Government GuidedSpecial Funds for Local Science and Technology Development (Shenzhen, China) (No. 2021Szvup043) and by Project of Jiangsu Province Science and Technology Plan Special Fund in 2022 (Key research and development plan industry foresight and key core technologies) under Grant BE2022064-1

语种：英文

外文关键词：Brain-computer interface; Motor imagery; Transfer learning; Domain selection; Feature alignment

摘要：In brain-computer interfaces (BCIs) based on motor imagery (MI), reducing calibration time is gradually becoming an urgent issue in practical applications. Recently, transfer learning (TL) has demonstrated its effectiveness in reducing calibration time in MI-BCI. However, the different data distribution of subjects greatly affects the application effect of TL in MI-BCI. Therefore, this paper combines data alignment, source domain selection, and feature alignment into the MI-TL. We propose a novel dual-step transfer framework based on source domain selection and feature alignment. First, the source and target domains are aligned using a pre-calibration strategy (PS), and then a sequential reverse selection method is proposed to match the optimal source domain for each target domain with the designed dual model selection strategy. We use filter bank regularization common space pattern (FBRCSP) to obtain more features and introduce manifold embedded distribution alignment (MEDA) to correct the prediction results of the support vector machine (SVM). The experimental results on two competition public datasets (BCI competition IV Dataset 1 and Dataset 2a) and our dataset show that the average classification accuracy of the proposed framework is higher than the baseline method (no domain selection and no feature alignment), which reaches 84.12%, 79.91%, and 78.45%, respectively. And the computational cost is reduced by half compared with the baseline method.