文献类型：期刊文献

英文题名：Dual branch neural network with dynamic learning mechanism for P300-based brain-computer interfaces

作者：Li, Shurui[1];Xu, Ren[2];Wang, Xingyu[3];Cichocki, Andrzej[4,5,6];Jin, Jing[1,3]

机构：[1]East China Univ Sci & Technol, Ctr Intelligent Comp, Sch Math, Shanghai 200237, Peoples R China;[2]G Tec Med Engn GmbH, A-4521 Schiedlberg, Austria;[3]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[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：2025

卷号：192

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20253018845368);WOS:【SCI-EXPANDED(收录号:WOS:001540728800001)】；

基金：This work was supported by Young Scientists Fund of the National Natural Science Foundation of China under Grant 62306111, 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.

语种：英文

外文关键词：Brain-computer interface; P300 signal; Class imbalance; Dual branch learning; Dynamic learning mechanism

摘要：Brain-computer interface (BCI) system offers an alternative or supplementary means of interaction for individuals with disabilities. P300 speller is a commonly utilized BCI system due to its high stability, and reliability and without intensive user training. Nevertheless, the inherent class imbalance within P300 datasets predisposes the system to overfit, potentially impacting the classification performances. Existing class rebalancing methods mainly rely on resampling or adjusting the class weight with a fixed value, thus it is still tricky to ensure that the output is evenly balanced. To mitigate the above class imbalance issue, this study proposes a dual branch learning (DBL) method that concurrently considers feature representation and class imbalance. This approach involves the ingestion of two distinct sample types-uniformly sampled and reverse-sampled data-into the feature extraction and classification modules during the training phase. Furthermore, a dynamic learning mechanism is implemented to incrementally emphasize minority class samples (specifically the P300 component) as training progresses. The effectiveness of the proposed DBL method is proved using both publicly accessible and self-collected datasets in a subject-dependent scheme. The proposed DBL method can achieve an accuracy of 97.37 % and 88.72 % in the above datasets. Besides, it provides superior and more reliable results compared with several deep learning and rebalancing methods. These findings highlight the promising potential of the proposed DBL framework in P300-based BCI.