数据采集与处理2026，Vol.41Issue(4)：981-994,14.DOI:10.16337/j.1004-9037.2026.04.005
基于EEG-fMRI多模态协变网络的运动想象神经表征效能映射建模
Mapping Between EEG-fMRI Multimodal Covariant Network and Neural Representation Efficacy of Motor Imagery
摘要
Abstract
Motor imagery(MI)is a key cognitive process in brain-computer interfaces and motor function rehabilitation,yet substantial individual differences exist in its behavioral performance.Previous studies,largely based on unimodal data,have struggled to reveal the brain network organization mechanisms with high spatiotemporal resolution that are closely associated with behavioral performance during MI.To address this gap,this study constructs a multimodal covariant network(MCN)based on electroencephalography(EEG)and functional magnetic resonance imaging(fMRI)data.We focus on analyzing the topological differences of MCN across three types of MI tasks,its predictive performance regarding individual imagery ability,and the differential network mechanisms between participants with high versus low task imagery ability,aiming to establish the mapping between EEG-fMRI spatiotemporal fusion features and neural representation efficacy of MI.The results show that the MCNs under all three action conditions exhibit clear task-dependent characteristics,with both left-hand and right-hand tasks demonstrating richer and more stable cross-subnetwork connections compared to the foot task,and the behavior-related network being most extensive for the right-hand task.Furthermore,based on the questionnaire scores,participants were divided into high and low MI ability groups.Between-group statistical differences reveal significant topological differences in MCN between the two groups across all three tasks,mainly distributed in the default mode network,sensorimotor network,visual network,and frontoparietal network.These findings demonstrate that MCN effectively captures the task specificity and individual differences in MI,providing new evidence for establishing the mapping between EEG-fMRI spatiotemporal information and the neural representation efficacy of MI from the perspective of multimodal fusion networks.关键词
运动想象/脑机接口/多模融合/EEG-fMRI/预测Key words
motor imagery(MI)/brain-computer interface(BCI)/multimodal fusion/EEG-fMRI/prediction分类
医药卫生引用本文复制引用
姜林,宋熙鹏,马姝岐,王广英,尧德中,徐鹏,卢竞,李发礼..基于EEG-fMRI多模态协变网络的运动想象神经表征效能映射建模[J].数据采集与处理,2026,41(4):981-994,14.基金项目
科技创新2030——重大项目(2022ZD0208901) （2022ZD0208901）
TCL青年学者计划. Science and Technology Innovation(STI)2030—Major Projects(No.2022ZD0208901) （STI）
TCL Young Scholar Pro-gram. （）
