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.Highlights：1. Propose a multimodal covariant network （MCN） framework that integrates EEG phase-locking value and fMRI beta-series correlation into a symmetric fusion architecture， enabling the unified characterization of cross-modal brain network organization during motor imagery （MI） without presupposing a dominant modality.2. Construct task-specific MCNs for three MI tasks， and systematically reveal hierarchical and task-dependent connectivity patterns， with hand tasks， particularly the right hand， exhibiting richer and more stable cross-subnetwork interactions than foot tasks.3. Design a nested leave-one-out cross-validation prediction pipeline combining least absolute shrinkage and selection operator （LASSO） regression and connectome-based predictive modeling （CPM）， demonstrating that MCN features can predict both subjective imagery ability （KVIQ-KI scores） and objective MI classification accuracy， with right-hand MI achieving the strongest predictive performance.