文献类型：期刊文献

英文题名：Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems

作者：Sun, Hao[1];Jin, Jing[1,2];Daly, Ian[3];Huang, Yitao[1];Zhao, Xueqing[1];Wang, Xingyu[1];Cichocki, Andrzej[4,5]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Shenzhen Res Inst, Shen Zhen 518063, Peoples R China;[3]Univ Essex, Sch Comp Sci & Elect Engn, Brain Comp Interfacing & Neural Engn Lab, Colchester CO4 3SQ, England;[4]RIKEN Brain Sci Inst, Wako 3510198, Japan;[5]Nicolaus Copernicus Univ UMK, PL-87100 Torun, Poland

年份：2023

卷号：399

外文期刊名：JOURNAL OF NEUROSCIENCE METHODS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001080100600001)】；

基金：This work was supported 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 National Government Guided Special Funds for Local Science and Technology Development (Shenzhen, China) (No. 2021Szvup043) and 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：Motor imagery (MI); Electroencephalogram (EEG); Feature learning; Graph representation; Self -attention

摘要：Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.