文献类型：期刊文献

英文题名：Self-distillation with beta label smoothing-based cross-subject transfer learning for P300 classification

作者：Li, Shurui[1];Zhao, Liming[2];Liu, Chang[3];Jin, Jing[1,3];Guan, Cuntai[4]

机构：[1]East China Univ Sci & Technol, Ctr Intelligent Comp, Sch Math, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China;[3]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[4]Nanyang Technol Univ, Coll Comp & Data Sci, Singapore 639798, Singapore

年份：2025

卷号：159

外文期刊名：PATTERN RECOGNITION

收录：;EI(收录号：20244517330084);WOS:【SCI-EXPANDED(收录号:WOS:001353763500001)】；

基金：This work was supported by Young Scientists Fund of the Na-tional Natural Science Foundation of China under Grant 62306111 and the China Postdoctoral Science Foundation, China under Grant 2023M741177, in part by Postdoctoral Fellowship Program of CPSF under Grant GZB20230216; in part 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 2021SHZDZX, in part by the Program of Introducing Talents of Discipline to Universities through the 111 Project under Grant B17017; This research is also supported 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. At the same time, the authors gratefully acknowledge financial support from China Scholarship Council, China.

语种：英文

外文关键词：Brain-computer interface; P300 classification; Cross-subject; Self-distillation

摘要：Background: The P300 speller is one of the most well-known brain-computer interface (BCI) systems, offering users a novel way to communicate with their environment by decoding brain activity. Problem: However, most P300-based BCI systems require a longer calibration phase to develop a subject- specific model, which can be inconvenient and time-consuming. Additionally, it is challenging to implement cross-subject P300 classification due to significant inter-individual variations. Method: To address these issues, this study proposes a calibration-free approach for P300 signal detection. Specifically, we incorporate self-distillation along with a beta label smoothing method to enhance model generalization and overall system performance, which can not only enable the distillation of informative knowledge from the electroencephalogram (EEG) data of other subjects but effectively reduce individual variability. Experimental results: The results conducted on the publicly available OpenBMI dataset demonstrate that the proposed method achieves statistically significantly higher performance compared to state-of-the-art approaches. Notably, the average character recognition accuracy of our method reaches up to 97.37% without the need for calibration. And information transfer rate and visualization further confirm its effectiveness. Significance: This method holds great promise for future developments in BCI applications.