文献类型：期刊文献

英文题名：Leveraging low-frequency components for enhanced high-frequency steady-state visual evoked potential based brain computer interface in fast calibration scenario

作者：Chen, Yixin[1,2];Xu, Ren[3];Lau, Andrew Ty[4];He, Xinjie[1,2];Chen, Weijie[1,2];Wang, Xingyu[1,2];Cichocki, Andrzej[5,6];Jin, Jing[1,2]

机构：[1]East China Univ Sci & Technol, Shanghai 200237, Peoples R China;[2]Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[3]G Tec Med Engn GmbH, Schiedlberg, Austria;[4]Shanghai Lansheng Brain Hosp Investment Co Ltd, Shanghai 200336, Peoples R China;[5]Polish Acad Sci, Syst Res Inst, 01-447b, Warsaw, Poland;[6]Nicolaus Copernicus Univ UMK, PL-87100 Torun, Poland

年份：2025

卷号：19

期号：1

外文期刊名：COGNITIVE NEURODYNAMICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001541864900002)】；

基金：The Grant National Natural Science Foundation of China, 62176090, STI 2030-major projects, 2022ZD0208900, Shanghai Municipal Science and Technology Major Project, 2021SHZDZX, Project of Jiangsu Province Science and Technology Plan Special Fund in 2022, BE2022064-1

语种：英文

外文关键词：Brain computer interface (BCI); Steady-state visual evoked potential (SSVEP); High-frequency; Transfer learning

摘要：High-frequency steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems offer improved user comfort but suffer from reduced performance compared to their low-frequency counterparts, limiting their practical application. To address this issue, we propose a transfer learning-based method that leverages low-frequency SSVEP data to enhance high-frequency SSVEP performance. A filtering mechanism is designed to extract informative components from low-frequency signals, and the least squares algorithm is employed to generate high-quality synthetic high-frequency data. Experiments conducted on two public datasets using TDCA, eTRCA, and advanced TRCA-based algorithms demonstrate significant performance improvements. Our approach requires only two calibration trials, achieving 9.03% and 14.49% accuracy increases for eTRCA and TDCA in Dataset 1, and 13.91% and 14.53% improvements in Dataset 2, all within 1.5 s. Moreover, our approach effectively addresses the issue of single calibration data for high-frequency SSVEP-BCI systems. These results support the feasibility of fast calibration and improved performance in real-world high-frequency BCI applications.