文献类型：期刊文献

英文题名：SecNet: A second order neural network for MI-EEG

作者：Liang, Wei[1];Allison, Brendan Z.[2];Xu, Ren[3];He, Xinjie[1];Wang, Xingyu[1];Cichocki, Andrzej[4,5,6];Jin, Jing[1,7]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]Univ Calif San Diego, Cognit Sci Dept, San Diego, CA USA;[3]Gtec Med Engn GmbH, Schiedlberg, Austria;[4]Polish Acad Sci, Syst Res Inst, Warsaw, Poland;[5]RIKEN Adv Intelligence Project, Tokyo, Japan;[6]Tokyo Univ Agr & Technol, Tokyo, Japan;[7]East China Univ Sci & Technol, Sch Math, Shanghai 200237, Peoples R China

年份：2025

卷号：62

期号：3

外文期刊名：INFORMATION PROCESSING & MANAGEMENT

收录：;EI(收录号：20245017498774);WOS:【SSCI(收录号:WOS:001375666500001)，SCI-EXPANDED(收录号:WOS:001375666500001)】；

基金：This work was supported by the STI 2030-major projects 2022ZD0208900 and Grant National Natural Science Foundation of China under Grant 62176090; 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 interfaces; Covariance Pooling; Motor Imagery; Riemannian Geometry; Attention Mechanism

摘要：Motor imagery-based brain-computer interfaces (MI-BCIs) hold significant potential for individuals with severe paralysis who are aware and alert by but unable to reliably control their muscles. MI-BCIs have garnered increasing attention from researchers in the field of motor function rehabilitation as a healthcare technology. However, the variability of inter-session and the difficulty in extracting energy features bring huge challenges to the information processing of such systems. To overcome this, we propose a novel architecture called SecNet, designed to capture relationships between convolutional features. SecNet utilizes multiple branches to learn spatio-temporal features of EEG signals and then pools them into a covariance. We integrate regularization and attention mechanisms to enhance the learning efficiency of features, followed by the adoption of a second order pooling method. Lastly, we employ Riemannian geometry learning to map features derived from symmetric positive definite covariance matrices. Evaluation experiments are conducted on a dataset from stroke patients and further compared its performance on two public datasets. Experimental results show that the SecNet is superior to the benchmark methods and achieves accuracy rates of 72.90%, 87.08% and 74.28% on the Stroke dataset, BCI IV 2a dataset and OpenBMI dataset, respectively. These results demonstrate its efficacy and robustness in inter-session decoding for MI-BCI, showing its practical utility for application. Our code is available at https://github.com/SecNet-mi/SecNet.