文献类型：期刊文献

英文题名：Decoding continuous motion trajectories of upper limb from EEG signals based on feature selection and nonlinear methods

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

机构：[1]East China Univ Sci & Technol, Sch Math, Shanghai 200237, Peoples R China;[2]G Tec Med Engn GmbH, A-4521 Schiedlberg, Austria;[3]Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland;[4]RIKEN Adv Intelligence Project, Tokyo 1030027, Japan;[5]Tokyo Univ Agr & Technol, Tokyo 1848588, Japan;[6]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China

年份：2024

卷号：21

期号：6

外文期刊名：JOURNAL OF NEURAL ENGINEERING

收录：;EI(收录号：20250117634189);WOS:【SCI-EXPANDED(收录号:WOS:001385884500001)】；

基金：This work was supported by Young Scientists Fund of the National Natural Science Foundation of China under Grant 62306111 and the China Postdoctoral Science Foundation, China under Grant 2023M741177, in part by Postdoctoral Fellowship Program of CPSF under Grant GZB20230216; 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; limb decoding; feature selection; polynomial regression

摘要：Objective. Brain-computer interface (BCI) system has emerged as a promising technology that provides direct communication and control between the human brain and external devices. Among the various applications of BCI, limb motion decoding has gained significant attention due to its potential for patients with motor impairment to regain independence and improve their quality of life. However, the reconstruction of continuous motion trajectories in BCI systems based on electroencephalography (EEG) signals remains a challenge in practical life. Approach. This study investigates the feasibility of applying feature selection and nonlinear regression for decoding motion trajectory from EEG. We propose to fix the time window, select the optimal feature set, and reconstruct the motion trajectory of motor execution tasks using polynomial regression. The proposed approach is validated on a public dataset consisting of EEG and hand position data recorded from 15 subjects. Several methods including ridge regression and multiple linear regression are employed as comparisons. Main results. The cross-validation results show that the proposed reconstructed method has the highest correlation with actual motion trajectories, with an average value of 0.511 +/- 0.019 ( p< 0.05). Significance. This finding demonstrates the great potential of our approach for real-world motor kinematics BCI applications.