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High Quality EEG Video Reconstruction Technique Based on Multi-Feature Fusion
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Xu-jiao ZHAO1, Yao LI2, Lin-hui SUN1, Yan-li YANG1, Hao GUO1, *
Science Technology and Engineering | 2025, 25(19) : 8117 - 8126
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Science Technology and Engineering | 2025, 25(19): 8117-8126
Papers∙Automation and Computational Technology
High Quality EEG Video Reconstruction Technique Based on Multi-Feature Fusion
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Xu-jiao ZHAO1, Yao LI2, Lin-hui SUN1, Yan-li YANG1, Hao GUO1, *
Affiliations
  • 1 College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Jinzhong 030600, China
  • 2 College of Software, Taiyuan University of Technology, Jinzhong 030600, China
Published: 2025-07-08 doi: 10.12404/j.issn.1671-1815.2405740
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In order to explore the connection between brain and vision and improve the clarity and accuracy of brain activity reconstruction video, a new method called high quality electroencephalogram video reconstruction (HQEEGVR) was proposed to reconstruct video from EEG (electroencephalogram) signals. Firstly, the masking spatio-temporal frequency fusion network (MSTFFNet), a three-branch EEG feature extraction network, was proposed to extract brain activity information from EEG signals and dig deeper into the semantics behind brain activity changes, spatio-temporal frequency information was extracted at the same time. Secondly, cross-modal contrast learning was introduced to align EEG, text and image features for use in the generation stage. Then, a cascade video diffusion model was proposed, specifically, the stable diffusion model was used to generate reference video frames based on EEG features, and then the video frames were used as references, motion vectors were integrated, and the video diffusion model was introduced to capture the video time features. High quality videos were ultimately generated. The results show that the model performs well in the reconstruction of the subject, motion, color and semantics of the video. It can be seen that the EEG signal can be used to capture the visual and semantic information of the brain activity, so as to reconstruct the video with high fidelity and visual authenticity.

EEG  /  MSTFFNet  /  stable diffusion model  /  video diffusion model  /  motion vector  /  video reconstruction
Xu-jiao ZHAO, Yao LI, Lin-hui SUN, Yan-li YANG, Hao GUO. High Quality EEG Video Reconstruction Technique Based on Multi-Feature Fusion[J]. Science Technology and Engineering, 2025 , 25 (19) : 8117 -8126 . DOI: 10.12404/j.issn.1671-1815.2405740
Year 2025 volume 25 Issue 19
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Article Info
doi: 10.12404/j.issn.1671-1815.2405740
  • Receive Date:2024-07-31
  • Online Date:2025-12-22
  • Published:2025-07-08
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  • Received:2024-07-31
  • Revised:2024-12-23
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    1 College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Jinzhong 030600, China
    2 College of Software, Taiyuan University of Technology, Jinzhong 030600, China
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小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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