文献类型：期刊文献

英文题名：Inter-participant transfer learning with attention based domain adversarial training for P300 detection

作者：Li, Shurui[1];Daly, Ian[2];Guan, Cuntai[3];Cichocki, Andrzej[4,5,6];Jin, Jing[1,7]

机构：[1]East China Univ Sci & Technol, Ctr Intelligent Comp, Sch Math, Shanghai 200237, Peoples R China;[2]Univ Essex, Sch Comp Sci & Elect Engn, Brain Comp Interfacing & Neural Engn Lab, Colchester CO4 3SQ, England;[3]Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore;[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;[7]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2024

卷号：180

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20240038427);WOS:【SCI-EXPANDED(收录号:WOS:001316516700001)】；

基金：This work was supported by Young Scientists Fund of the National Natural Science Foundation of China under Grant 62306111, the China Postdoctoral Science Foundation under Grant 2023M741177, and Postdoctoral Fellowship Program of CPSF under Grant GZB20230216, in part 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. 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, 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：Brain-computer interface; P300 detection; Cross participant task; Domain generalization

摘要：A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which requires training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source domain transfer learning method based Attention Domain Adversarial Neural Network (OADANN) to mitigate data distribution discrepancies for cross-participant classification tasks. We train and validate our proposed model on both a publicly available OpenBMI dataset and a Self-collected dataset, employing a leave one participant out cross validation scheme. Experimental results demonstrate that the proposed OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method.