Modeling Spatiotemporal Neural Frames for High Resolution Brain Dynamic
arXiv:2603.24176v2 Announce Type: replace Abstract: Capturing dynamic spatiotemporal neural activity is essential for understanding large-scale brain mechanisms. Functional magnetic resonance imaging (fMRI) provides high-resolution cortical representations that form a strong basis for characterizing fine-grained brain activity patterns. The high acquisition cost of fMRI limits large-scale applications, therefore making high-quality fMRI reconstruction a crucial task. Electroencephalography (EEG) offers millisecond-level temporal cues that complement fMRI. Leveraging this complementarity, we present an EEG-conditioned framework for reconstructing dynamic fMRI as continuous neural sequences with high spatial fidelity and strong temporal coherence at the cortical-vertex level. To address samp
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Abstract:Capturing dynamic spatiotemporal neural activity is essential for understanding large-scale brain mechanisms. Functional magnetic resonance imaging (fMRI) provides high-resolution cortical representations that form a strong basis for characterizing fine-grained brain activity patterns. The high acquisition cost of fMRI limits large-scale applications, therefore making high-quality fMRI reconstruction a crucial task. Electroencephalography (EEG) offers millisecond-level temporal cues that complement fMRI. Leveraging this complementarity, we present an EEG-conditioned framework for reconstructing dynamic fMRI as continuous neural sequences with high spatial fidelity and strong temporal coherence at the cortical-vertex level. To address sampling irregularities common in real fMRI acquisitions, we incorporate a null-space intermediate-frame reconstruction, enabling measurement-consistent completion of arbitrary intermediate frames and improving sequence continuity and practical applicability. Experiments on the CineBrain dataset demonstrate superior voxel-wise reconstruction quality and robust temporal consistency across whole-brain and functionally specific regions. The reconstructed fMRI also preserves essential functional information, supporting downstream visual decoding tasks. This work provides a new pathway for estimating high-resolution fMRI dynamics from EEG and advances multimodal neuroimaging toward more dynamic brain activity modeling.
Comments: CVPR 2026
Subjects:
Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Neurons and Cognition (q-bio.NC)
Cite as: arXiv:2603.24176 [eess.IV]
(or arXiv:2603.24176v2 [eess.IV] for this version)
https://doi.org/10.48550/arXiv.2603.24176
arXiv-issued DOI via DataCite
Submission history
From: Wanying Qu [view email] [v1] Wed, 25 Mar 2026 10:53:11 UTC (6,835 KB) [v2] Tue, 31 Mar 2026 09:59:29 UTC (6,835 KB)
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