With the advancement of non-invasive brain–computer interface (BCI) technologies, decoding high-level cognitive activity has become pivotal for expanding human–machine interaction. Visual imagery-based BCI (VI-BCI) enable voluntary activation of specific brain regions without external cue, offering novel pathways for immersive applications. However, research on the neural representation of such complex cognitive tasks is still limited, and most existing electroencephalogram (EEG) datasets primarily target motor imagery, hindering the development of robust VI decoding models. Here we present an EEG dataset recorded from 22 participants performing visual imagery tasks involving ten commonly recognized images across three categories: figures, animals, and objects. Each participant completed two sessions, with EEG recorded from 32-channels at 1000 Hz. This resource helps overcome data homogeneity issues in VI studies and provides a foundation for exploring neuroplasticity, adaptive decoding algorithms, and cross-subject generalization, facilitating the transition from controlled experiments to real-world applications.

Background: Visual imagery (VI) is a cognitive process that generates visual representations in the absence of external visual input; however, the spatiotemporal organization of its neural representations remains poorly understood. Methods: In this study, we combined electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) with representational similarity analysis (RSA) and the Schaefer 2018 functional parcellation atlas to compare the correspondence between brain network representations and CORnet-S model representations during visual perception (VP) and VI. We further examined the cross-modal associations between EEG time–frequency representations and fMRI network representations during VI. Results: Significant model–brain representational correspondence was detected in the visual network during VP, and no significant correspondence was detected in any network during VI, yielding different descriptive significance profiles across the two tasks. Cross-modal EEG–fMRI RSA further revealed frequency- and time-dependent representational correspondence involving the frontoparietal, default mode, ventral attention, visual, and limbic networks. Significant cross-modal correspondence was observed across the alpha, beta, and gamma bands, with descriptive variation in the networks and temporal clusters involved in each frequency band. Conclusions: Collectively, these findings suggest that VI is not merely a reinstatement of VP but is associated with distributed representational patterns involving multiple functional networks. This study provides multimodal evidence for understanding the neural mechanisms underlying VI.