文献类型：期刊文献

英文题名：An Interpretable Regression Method for Upper Limb Motion Trajectories Detection With EEG Signals

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

机构：[1]East China Univ Sci & Technol, Sch Math, Shanghai, Peoples R China;[2]G Tec Med Engn GmbH, Schiedlberg, Austria;[3]Polish Acad Sci, Syst Res Inst, Warsaw, Poland;[4]RIKEN Adv Intelligence Project, Chuo, Japan;[5]Tokyo Univ Agr & Technol, Tokyo, Japan

年份：2025

卷号：72

期号：10

起止页码：2961

外文期刊名：IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

收录：;EI(收录号：20251518234123);WOS:【SCI-EXPANDED(收录号:WOS:001577047700002)】；

基金：This work was supported in part by the Young Scientists Fund of the National Natural Science Foundation of China under Grant 62306111, in part by China Postdoctoral Science Foundation under Grant 2023M741177, in part by the Postdoctoral Fellowship Program of CPSF under Grant GZB20230216, in part by the Grant National Natural Science Foundation of China under Grant 62176090, in part by STI 2030-major Projects under Grant 2022ZD0208900, 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, and in part by the 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：Electroencephalography; Trajectory; Hands; Motors; Kinematics; Brain modeling; Feature extraction; Elbow; Training; Three-dimensional displays; Brain-computer interface; limb decoding; interpretable framework; motion trajectory reconstruction

摘要：Objective: The motion trajectory prediction (MTP) based brain-computer interface (BCI) leverages electroencephalography (EEG) signals to reconstruct the three-dimensional trajectory of upper limb motion, which is pivotal for the advancement of prosthetic devices that can assist motor-disabled individuals. Most research focused on improving the performance of regression models while neglecting the correlation between the implicit information extracted from EEG features across various frequency bands with limb kinematics. Current work aims to identify key channels that capture information related to various motion execution movements from different frequency bands and reconstruct three-dimensional motion trajectories based on EEG features. Methods: We propose an interpretable motion trajectory regression framework that extracts bandpower features from different frequency bands and concatenates them into multi-band fusion features. The extreme gradient boosting regression model with Bayesian optimization and Shapley additive explanation methods are introduced to provide further explanation. Results: The experimental results demonstrate that the proposed method achieves a mean Pearson correlation coefficient (PCC) value of 0.452, outperforming traditional regression models. Conclusion: Our findings reveal that the contralateral side contributes the most to motion trajectory regression than the ipsilateral side which improves the clarity and interpretability of the motion trajectory regression model. Specifically, the feature from channel C5 in the Mu band is crucial for the movement of the right hand, while the feature from channel C3 in the Beta band plays a vital role. Significance: This work provides a novel perspective on the comprehensive study of movement disorders.