DynaMind: Reconstructing Dynamic Visual Scenes from EEG by Aligning Temporal Dynamics and Multimodal Semantics to Guided Diffusion
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| Format: | Preprint |
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2025
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| _version_ | 1866916928047546368 |
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| author | Liu, Junxiang Lin, Junming Li, Jiangtong Li, Jie |
| author_facet | Liu, Junxiang Lin, Junming Li, Jiangtong Li, Jie |
| contents | Reconstruction dynamic visual scenes from electroencephalography (EEG) signals remains a primary challenge in brain decoding, limited by the low spatial resolution of EEG, a temporal mismatch between neural recordings and video dynamics, and the insufficient use of semantic information within brain activity. Therefore, existing methods often inadequately resolve both the dynamic coherence and the complex semantic context of the perceived visual stimuli. To overcome these limitations, we introduce DynaMind, a novel framework that reconstructs video by jointly modeling neural dynamics and semantic features via three core modules: a Regional-aware Semantic Mapper (RSM), a Temporal-aware Dynamic Aligner (TDA), and a Dual-Guidance Video Reconstructor (DGVR). The RSM first utilizes a regional-aware encoder to extract multimodal semantic features from EEG signals across distinct brain regions, aggregating them into a unified diffusion prior. In the mean time, the TDA generates a dynamic latent sequence, or blueprint, to enforce temporal consistency between the feature representations and the original neural recordings. Together, guided by the semantic diffusion prior, the DGVR translates the temporal-aware blueprint into a high-fidelity video reconstruction. On the SEED-DV dataset, DynaMind sets a new state-of-the-art (SOTA), boosting reconstructed video accuracies (video- and frame-based) by 12.5 and 10.3 percentage points, respectively. It also achieves a leap in pixel-level quality, showing exceptional visual fidelity and temporal coherence with a 9.4% SSIM improvement and a 19.7% FVMD reduction. This marks a critical advancement, bridging the gap between neural dynamics and high-fidelity visual semantics. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_01177 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DynaMind: Reconstructing Dynamic Visual Scenes from EEG by Aligning Temporal Dynamics and Multimodal Semantics to Guided Diffusion Liu, Junxiang Lin, Junming Li, Jiangtong Li, Jie Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Signal Processing Reconstruction dynamic visual scenes from electroencephalography (EEG) signals remains a primary challenge in brain decoding, limited by the low spatial resolution of EEG, a temporal mismatch between neural recordings and video dynamics, and the insufficient use of semantic information within brain activity. Therefore, existing methods often inadequately resolve both the dynamic coherence and the complex semantic context of the perceived visual stimuli. To overcome these limitations, we introduce DynaMind, a novel framework that reconstructs video by jointly modeling neural dynamics and semantic features via three core modules: a Regional-aware Semantic Mapper (RSM), a Temporal-aware Dynamic Aligner (TDA), and a Dual-Guidance Video Reconstructor (DGVR). The RSM first utilizes a regional-aware encoder to extract multimodal semantic features from EEG signals across distinct brain regions, aggregating them into a unified diffusion prior. In the mean time, the TDA generates a dynamic latent sequence, or blueprint, to enforce temporal consistency between the feature representations and the original neural recordings. Together, guided by the semantic diffusion prior, the DGVR translates the temporal-aware blueprint into a high-fidelity video reconstruction. On the SEED-DV dataset, DynaMind sets a new state-of-the-art (SOTA), boosting reconstructed video accuracies (video- and frame-based) by 12.5 and 10.3 percentage points, respectively. It also achieves a leap in pixel-level quality, showing exceptional visual fidelity and temporal coherence with a 9.4% SSIM improvement and a 19.7% FVMD reduction. This marks a critical advancement, bridging the gap between neural dynamics and high-fidelity visual semantics. |
| title | DynaMind: Reconstructing Dynamic Visual Scenes from EEG by Aligning Temporal Dynamics and Multimodal Semantics to Guided Diffusion |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Signal Processing |
| url | https://arxiv.org/abs/2509.01177 |