文献类型：期刊文献

英文题名：A Transfer Learning SSVEP Decoding Algorithm Calibrated With Single-Trial Data

作者：Jin, Jing[1,2];Qin, Ke[2];Allison, Brendan Z.[3];Li, Shurui[1];Zhang, Yutao[2];Wang, Xingyu[2];Cichocki, Andrzej[4,5,6]

机构：[1]East China Univ Sci & Technol, Sch Math, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]Univ Calif San Diego, Dept Cognit Sci, La Jolla, CA 92093 USA;[4]Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland;[5]RIKEN Adv Intelligence Project, Tokyo 1030027, Japan;[6]Tokyo Univ Agr & Technol, Tokyo 1848588, Japan

年份：2026

卷号：37

期号：3

起止页码：1191

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20254319398186);WOS:【SCI-EXPANDED(收录号:WOS:001600853800001)】；

基金：This work was supported in part by the Grant Science and Technology Innovation (STI) 2030-Major Projects under Grant 2022ZD0208900; in part by the National Natural Science Foundation of China under Grant 62176090; in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX; and in part by the Project of Jiangsu Province Science and Technology Plan Special Fund in 2022 (Key Research and Development Plan Industry Foresight, Fundamental Research Fund for the Central Universities JKH01241605 and Key Core Technologies) under Grant BE2022064-1.

语种：英文

外文关键词：Calibration; Decoding; Spatial filters; Transfer learning; Electroencephalography; Correlation; Visualization; Vectors; Steady-state; Signal to noise ratio; Brain-computer interfaces (BCIs); cross-dataset; data mismatch problem; steady-state visual-evoked potential (SSVEP); transfer learning (TL)

摘要：Training-based algorithms significantly outperform training-free methods in terms of recognition performance for steady-state visual-evoked potential (SSVEP)-based brain-computer Interfaces (BCIs). However, collecting training data requires calibration experiments that are effort-intensive and often costly. These calibration demands limit the practicality of BCI, as users (and even system operators) may experience fatigue or lose interest in continued use. Transfer learning (TL) offers an effective solution, but it typically relies on either a certain amount of target domain data or extensive source domain data. To address this limitation, we introduce the concept of cross-dataset TL in SSVEP for the first time to extract transfer knowledge from other datasets. During this process, we identified a data mismatch problem that severely compromises the generalizability of transfer knowledge. To overcome this challenge, we propose a TL-SSVEP decoding algorithm calibrated with single-trial data (TL-CSTD). Specifically, we use 2 s of 8 Hz single-trial calibration data from the target domain to obtain matched transfer templates from the source domain. These templates are then corrected to extract holistic and single-period transfer knowledge, which are subsequently employed to construct an efficient TL-SSVEP decoding model for the target subject. Experimental results on three large SSVEP datasets demonstrate that TL-CSTD effectively addresses the data mismatch problem and achieves excellent SSVEP recognition performance using only 2 s of single-trial calibration data, showing its significant application potential and practicality.