文献类型：期刊文献

英文题名：The Spatio-Temporal Equalization Sliding-Window Distribution Distance Maximization Based on Unsupervised Learning for Online Event-Related Potential-Based Brain-Computer Interfaces

作者：Wang, Haoye[1];Jin, Jing[1,2];He, Xinjie[1];Li, Shurui[2];Cichocki, Andrzej[3,4,5,6]

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, Sch Math, Shanghai 200237, Peoples R China;[3]Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland;[4]Nicolaus Copernicus Univ UMK, PL-87100 Torun, Poland;[5]RIKEN Adv Intelligence Project, Tokyo 1030027, Japan;[6]Tokyo Univ Agr & Technol, Tokyo 1848588, Japan

年份：2025

卷号：13

期号：4

外文期刊名：MACHINES

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001475123500001)】；

基金：This work was supported by the Grant National Natural Science Foundation of China under Grant 62176090 and STI 2030-major projects 2022ZD0208900; in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX; and 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 JKH01231636 and key core technologies) under Grant BE2022064-1.

语种：英文

外文关键词：event-related potential; spatio-temporal equalization; unsupervised classification

摘要：Brain-computer interfaces (BCIs) provide a direct communication pathway between the central nervous system and external environments, enabling human-machine interaction control. Among them, event-related potential (ERP)-based BCIs are among the most accurate and reliable BCI systems. However, current mainstream classification algorithms struggle to eliminate calibration requirements and rely heavily on costly labeled data, limiting the practical usability of ERP-based BCIs. To address this, the development of unsupervised algorithms is critical for advancing real-world BCI applications. In this study, we propose the spatio-temporal equalization sliding-window distribution distance maximization (STE-sDDM) algorithm, which introduces spatio-temporal equalization (STE) to unsupervised ERP classification for the first time and integrates it with a novel unsupervised classification method, sliding-window distribution distance maximization (sDDM). STE estimates and removes colored noise interference in background noise to enhance the signal-to-noise ratio of inputs for sDDM. Meanwhile, sDDM leverages an enhanced inter-class divergence metric based on the ergodic hypothesis theory, utilizing sliding windows to emphasize temporally discriminative features, thereby improving unsupervised classification accuracy. The experimental results demonstrate that the integration of STE and sDDM significantly enhances ERP feature separability, outperforming state-of-the-art unsupervised online classification algorithms in spelling accuracy and the information transfer rate (ITR), facilitating more accurate and faster plug-and-play real-time control for BCI systems. Additionally, static spatio-temporal equalizer architectures were found to outperform dynamic architectures when combined with this framework.