文献类型：期刊文献

英文题名：A Domain Generalization Method for EEG Based on Domain-Invariant Feature and Data Augmentation

作者：Jin, Jing[1,2];Li, Junxian[2];Pan, Xiaochuan[2];Xu, Ren[3];Cichocki, Andrzej[4];Du, Wenli[1];Qian, Feng[1]

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]Univ Appl Sci Campus Vienna, Dept Engn, A-1100 Vienna, Austria;[4]Nicolaus Copernicus Univ, Polish Acad Sci, Syst Res Inst, Warsaw PL-01447B, Poland

年份：2026

卷号：7

外文期刊名：CYBORG AND BIONIC SYSTEMS

收录：;EI(收录号：20260920169420);WOS:【SCI-EXPANDED(收录号:WOS:001697548600001)】；

基金：This work was supported by Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project 2022ZD0208900 and National Natural Science Foundation of China under grant 62176090 and 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 and in part by the Lingang Laboratory under grant no. LGL8998.

语种：英文

外文关键词：Biomedical signal processing - Brain - Brain computer interface - Electroencephalography - Electrophysiology - Interfaces (computer) - Neurophysiology - Personnel training

摘要：Brain-computer interface (BCI) technology, which controls external devices by directly decoding brain activities, has made important progress and practical applications in recent years in many fields. However, the domain bias issue in cross-domain applications remains a significant challenge in the practical implementation of BCI technology. This is particularly acute in scenarios where target data are unavailable, largely because of the noise sensitivity and acquisition limitations inherent in electroencephalography (EEG) signal data. When processing nonstationary EEG signals, existing domain generalization methods face limitations: Adversarial training may compromise model stability, while global feature alignment approaches struggle to sufficiently decouple category-dependent and category-independent features, thereby constraining generalization performance. Therefore, in this paper, we propose a hybrid approach based on domain-invariant feature learning and data enhancement. We introduce a "fixed" structure enhancement method that combines domain-invariant feature learning with data enhancement strategies, decouples domain-invariant features from other features, optimizes cross-domain feature extraction, and reduces the effect of noise in data. Through extensive experimental validation on multiple publicly available datasets, the model proposed in this paper outperforms the existing state-of-the-art methods, providing a novel and effective solution to the domain bias problem in BCI.