A Review of Joint EEG-fMRI Methods for Visual Evoked Response Studies

SignificanceThe study of Visual Evoked Responses (VERs) using non-invasive neuroimaging is central to understanding human visual information processing. Electroencephalography (EEG) provides millisecond temporal resolution but has limited spatial precision. Functional Magnetic Resonance Imaging (fMRI) offers millimeter spatial resolution based on the blood-oxygen-level-dependent signal, although its temporal resolution is constrained by delayed hemodynamic responses. This trade-off limits the ability of any single modality to characterize complex visual processes such as attentional modulation, motion perception, and multisensory integration. Joint EEG-fMRI acquisition has therefore become an effective multimodal approach. By recording both modalities synchronously, this technique combines their complementary strengths and yields a unified spatiotemporal representation of visual neural dynamics. Despite increasing use, the literature lacks a focused review that summarizes core methods, representative applications, and continuing challenges in joint EEG-fMRI research on VERs. This review addresses this need by providing a structured overview for researchers working on visual system investigation.ProgressThe review first introduces the foundational technologies that support joint EEG-fMRI studies, beginning with synchronous data acquisition using MR-compatible EEG systems and dedicated synchronization hardware. The core data fusion methods are grouped into asymmetric and symmetric approaches. Asymmetric strategies use one modality to constrain analyses of the other. EEG-informed fMRI analysis models fMRI activity using single-trial EEG features, whereas fMRI-informed EEG source imaging uses fMRI activation maps as spatial priors to improve source localization. Symmetric fusion treats both modalities equally. Data-driven methods such as joint independent component analysis identify shared neural sources without imposing strong biophysical assumptions. These methods have contributed to advances in several areas. In visual mechanism studies, joint EEG-fMRI has clarified feedforward and feedback interactions in visual cortical networks. In clinical diagnosis and evaluation, it offers objective physiological markers for disorders such as amblyopia and epilepsy by revealing altered activation patterns and network dysfunction. In Brain-Computer Interface (BCI) research, multimodal feature fusion improves the accuracy and robustness of decoding visual intentions.ConclusionsThis review examines joint EEG-fMRI methods for VER studies, classifying major acquisition and fusion strategies and summarizing representative applications. The choice of fusion framework depends on the research objective, data quality, and underlying assumptions. Although joint EEG-fMRI benefits basic neuroscience, clinical diagnosis, and BCI development, several issues limit broader use. System-level obstacles include hardware-induced artifacts, particularly severe electromagnetic interference in ultra-high-field MRI, which degrades EEG data quality. Algorithmic challenges arise from the mismatch in spatiotemporal scales between rapid EEG signals and delayed hemodynamic responses. Inter-subject variability further reduces the generalizability of analytical and decoding models. Continued innovation in hardware engineering and computational methods is required to address these limitations.ProspectsFuture work in joint EEG-fMRI for VER studies is expected to progress gradually and will be shaped by advances in artificial intelligence. System-level developments include next-generation hardware combining ultra-high-field MRI systems with artifact-resilient EEG sensors and real-time correction algorithms. The creation of open, multi-center EEG-fMRI databases (following standards like BIDS) based on standardized formats and analysis pipelines will improve reproducibility and comparability. Algorithmic progress is likely to focus on artificial intelligence and deep learning. End-to-end neural architectures with spatiotemporal attention mechanisms may learn nonlinear transformations between EEG and fMRI directly, addressing limitations of conventional linear models. Transfer learning and personalized modeling may mitigate inter-subject variability and support adaptive decoding and clinical applications. As clinical and BCI uses expand, balancing model complexity with interpretability and computational efficiency will remain essential. These developments are expected to advance understanding of visual neural computation, improve diagnostic and therapeutic strategies, and support more effective BCI systems.