视觉诱发响应研究中EEG与fMRI联合应用方法综述

危志伟; 肖晓琳; 许敏鹏; 明东

doi:10.11999/JEIT250781

视觉诱发响应研究中EEG与fMRI联合应用方法综述

doi: 10.11999/JEIT250781 cstr: 32379.14.JEIT250781

危志伟¹,
肖晓琳^{1, 2, ,},
许敏鹏^{1, 2},
明东^{1, 2}

1.
天津大学精密仪器与光电子工程学院天津 300072
2.
天津大学医学工程与转化医学研究院天津 300072

基金项目: 国家自然科学基金(W2511072, 62106170)

详细信息

作者简介:
危志伟：男，硕士生，研究方向为视觉脑机接口

肖晓琳：女，博士，副教授，研究方向为视觉型脑机接口、高速高维脑操控系统、视觉脑控游戏、临床视功能检测

许敏鹏：男，博士，教授，研究方向为脑-机接口，神经信号处理和神经调控

明东：男，博士，教授，研究方向为脑-机接口、神经再生与修复、神经仿生与智能、神经刺激与调节、神经传感与成像

通讯作者:
肖晓琳　xiaoxiao0@tju.edu.cn

中图分类号: TN99; R741.044
计量
- 文章访问数: 22
- HTML全文浏览量: 10
- PDF下载量: 2
- 被引次数: 0
出版历程
- 修回日期: 2025-12-02
- 录用日期: 2025-12-02
- 网络出版日期: 2026-01-10

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

WEI Zhiwei¹,
XIAO Xiaolin^{1, 2
, ,},
XU Minpeng^{1, 2},
MING Dong^{1, 2}

1.
School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, China
2.
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China

Funds: The National Natural Science Foundation of China (W2511072, 62106170)

摘要

摘要: 利用脑电图(EEG)和功能性磁共振成像(fMRI)等无创脑成像技术研究视觉诱发响应，是探索人类视觉信息加工机制的重要途径。EEG-fMRI联合技术综合了EEG的高时间分辨优势与fMRI的高空间分辨优势，从更全面的神经时空活动视角为视觉诱发响应研究提供了方法支撑。该文系统综述了视觉诱发响应研究中EEG和fMRI的经典融合方法和EEG-fMRI联合技术在神经科学领域的应用情况，最后讨论了EEG-fMRI联合应用方法在视觉诱发响应研究中面临的技术挑战和未来发展方向。
- EEG-fMRI联合技术 /
- 视觉诱发响应 /
- 脑电 /
- 功能性磁共振成像 /
- 数据融合与分析
Abstract: Significance The study of visual evoked responses (VERs) using non-invasive neuroimaging techniques is a cornerstone of neuroscience, providing critical insights into the mechanisms of human visual information processing. Among the available modalities, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are paramount. EEG captures neural electrical activity with millisecond-level temporal resolution but is fundamentally limited by its poor spatial localization capabilities. Conversely, fMRI provides millimeter-level spatial precision by measuring the blood-oxygen-level-dependent (BOLD) signal, yet its temporal resolution is inherently constrained by the sluggish nature of hemodynamic responses. This intrinsic trade-off between temporal and spatial resolution significantly hampers the ability of any single modality to fully elucidate complex visual processes such as attentional modulation, motion perception, and multi-sensory integration. To overcome this bottleneck, the joint application of EEG and fMRI has emerged as a powerful multimodal approach. By synchronously acquiring both datasets, this integrated technique synergistically combines the distinct strengths of each modality, offering a comprehensive spatiotemporal perspective on the complex dynamics of visual neural networks. Despite its growing adoption, existing literature often lacks a focused, systematic review that specifically details the core methodologies, illustrates key applications, and outlines the persistent challenges and future trends of joint EEG-fMRI in VER research. This review aims to fill this gap by providing a comprehensive and structured overview of the field, serving as a foundational reference for researchers seeking to leverage this advanced technique to explore the visual system. Progress This review first elaborates on the foundational technologies that enable joint EEG-fMRI studies, starting with the synchronous acquisition of data. This is addressed through MR-compatible EEG systems and dedicated synchronization hardware. The core of the review then systematically analyzes data fusion methodologies, which are categorized into asymmetric and symmetric approaches. Asymmetric fusion uses one modality to constrain the analysis of the other, exemplified by EEG-informed fMRI analysis, which uses single-trial EEG features to model fMRI data, and fMRI-informed EEG source imaging, which uses fMRI activation maps as spatial priors to enhance source localization accuracy. In contrast, symmetric fusion treats both modalities equally, with data-driven techniques like joint independent component analysis (joint ICA) being widely adopted to reveal shared underlying neural sources without strong biophysical assumptions. The application of these methodologies has yielded significant breakthroughs across multiple domains. In visual mechanism analysis, the technique has been instrumental in dissecting the complex feedforward and feedback dynamics of cortical areas involved in vision. In clinical diagnosis and evaluation, joint EEG-fMRI provides objective neurophysiological biomarkers for visual disorders like amblyopia and epilepsy by identifying distinct patterns of cortical activation deficits and network dysfunctions. In the field of brain-computer interfaces (BCIs), the fusion of multimodal features has significantly improved the accuracy and robustness of decoding visual intentions. Conclusions This review critically examines the joint EEG-fMRI landscape for VER studies, systematically classifying the key data acquisition and fusion methodologies and highlighting their representative applications. The analysis reveals that the choice of an optimal fusion strategy—be it asymmetric or symmetric, data-driven or model-driven—is highly dependent on the specific research question, available data quality, and underlying assumptions. While the technique has proven useful in advancing basic neuroscience, clinical diagnostics, and BCI development, its broader adoption is still hindered by persistent challenges. At the system level, hardware-induced artifacts, particularly the severe electromagnetic interference in ultra-high-field MRI environments, remain a major technical obstacle that compromises data quality. At the algorithmic level, the inherent mismatch in spatiotemporal scales between the fast, transient EEG signals and the slow, delayed BOLD response continues to pose a core fusion challenge. This is further complicated by high inter-subject variability in neural responses, which limits the generalizability of analytical models and decoding algorithms across individuals. These limitations underscore the need for continued innovation in both hardware engineering and computational methods to unlock the full potential of this powerful multimodal technique. Prospects Looking ahead, the research landscape for joint EEG-fMRI methods in VER studies is poised for significant evolution, constituting a long-term and complex process. With the integration of emerging technologies such as artificial intelligence, the methodological frameworks in this domain will evolve toward greater intelligence and automation. System-level trends point toward the development of next-generation hardware, including ultra-high-field MRI systems combined with artifact-immune EEG sensors and real-time artifact correction algorithms. Furthermore, the establishment of open-access, multi-center EEG-fMRI databases (following standards like BIDS) and standardized analysis pipelines will be crucial for improving the reproducibility and comparability of research findings, fostering a collaborative ecosystem. Algorithm-level trends are increasingly centered on the integration of artificial intelligence and deep learning. End-to-end neural network architectures, such as those incorporating spatiotemporal attention mechanisms, hold the promise of learning the complex, non-linear transformations between EEG and fMRI data directly, thus overcoming the limitations of traditional linear models. Moreover, leveraging transfer learning and personalized modeling frameworks can address the challenge of inter-subject variability, leading to the development of adaptive and robust models for visual decoding and clinical applications. Concurrently, as clinical and BCI applications accelerate, the critical challenge of balancing model complexity with interpretive clarity and computational efficiency warrants in-depth investigation. Ultimately, these synergistic advancements in hardware and algorithms will deepen our understanding of the visual system’s computational principles, refine the diagnosis and treatment of visual disorders, and propel the development of more intuitive and powerful brain-computer interfaces.
- EEG-fMRI integration /
- Visual evoked response (VER) /
- Electroencephalography (EEG) /
- Functional Magnetic Resonance Imaging (fMRI) /
- Data fusion and analysis

HTML全文

图 1 1999～2024 EEG-fMRI联合应用出版文献数量与被引情况

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图 2 EEG-fMRI联合应用相关文献按WOS中主题分析的结果

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图 3 同步EEG-fMRI采集系统结构简图

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图 4 EEG与fMRI数据的对称融合和非对称融合

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图 5 基于GLM模型的EEG-informed fMRI示意图

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图 6 基于源重建的fMRI-informed EEG示意图

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图 7 基于joint ICA的EEG-fMRI对称融合示意图

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