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To address the limitations of existing approaches that rely heavily on voxel\u2010level high\u2010dimensional modeling or complex physiological assumptions, we propose a Hierarchical Temporal Attention Network (HTAN) for EEG\u2010to\u2010fMRI prediction, which takes ROI\u2010level BOLD signal generation as its primary modeling objective and explicitly characterizes the temporal dependencies between multiscale EEG neural dynamics and hemodynamic responses. HTAN constructs EEG temporal context inputs aligned with the fMRI sampling rate and incorporates hierarchical temporal encoding and attention mechanisms to effectively learn the contribution weights of EEG features across multiple temporal scales to ROI\u2010level BOLD responses. Experimental results on synchronized EEG\u2013fMRI datasets demonstrate that HTAN achieves a group\u2010level mean scan\u2010wise Pearson correlation coefficient of 0.82\u2009\u00b1\u20090.02, with individual subject performance consistently distributed between 0.80 and 0.86, indicating strong cross\u2010subject generalization. By further projecting the predicted ROI\u2010level BOLD signals into anatomical voxel space to generate pseudo\u2010fMRI visualizations, spatial consistency analyses reveal stable and reproducible activation patterns within functionally relevant brain regions. It should be emphasized that this procedure serves solely as a spatial representation of ROI\u2010level predictions and does not constitute direct voxel\u2010wise fMRI reconstruction. These results demonstrate that, without resorting to voxel\u2010level high\u2010dimensional generation or explicit hemodynamic modeling, HTAN can robustly uncover the temporal mapping between EEG and fMRI BOLD signals through structured temporal modeling and function\u2010oriented ROI\u2010level analysis, providing a stable and neurobiologically interpretable paradigm for EEG\u2013fMRI cross\u2010modal prediction.<\/jats:p>","DOI":"10.1002\/cpe.70860","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T07:18:17Z","timestamp":1784013497000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>EEG<\/scp>\n                    \u2010To\u2010\n                    <scp>fMRI<\/scp>\n                    Cross\u2010Modal Prediction via Hierarchical Temporal Attention Network: Toward Accessible Functional Neuroimaging"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3008-3444","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering Xi'an Polytechnic University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering Xi'an Polytechnic University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering Xi'an Polytechnic University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manfredo","family":"Atzori","sequence":"additional","affiliation":[{"name":"University of Applied Sciences Western Switzerland (HES\u2010SO)  Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henning","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"University of Applied Sciences Western Switzerland (HES\u2010SO)  Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.959557"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainres.2021.147390"},{"issue":"5","key":"e_1_2_9_4_1","first-page":"544","article-title":"Research on Construction of Knowledge Base for MRI Equipment Maintenance Based on Knowledge Graph","volume":"48","author":"Chu C.","year":"2024","journal-title":"Zhongguo Yi Liao Qi Xie Za Zhi = Chinese Journal of Medical Instrumentation"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci15091013"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22062262"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2016.00476"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.22623"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2025.3550206"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"K.RoosandA.Fukuda From Brainwaves to Brain Scans: a Robust Neural Network for EEG\u2010to\u2010fMRI Synthesis. 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