← 研踪 PaperTrailCVF · CVPR 2026:cvf:85d32a49d9831682840f5d62fc3259cd · 记录 #1
Cross-Subject EEG-to-Video Reconstruction and Beyond
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 03:35
摘要
Reconstructing video content from EEG (electroencephalogram) is a research task of significant scientific importance. However, due to inter-subject differences in physiological states and variations in signal acquisition configurations, this task faces the challenge of inconsistent cross-subject generation.To address this, we propose a Subject Adversarial and Mapping Network (SAM-Net). In SAM-Net, we first introduce a Hybrid Region-Temporal (HRT) Encoder to conduct inter-channel semantic interactions guided by brain regions and aggregate temporal semantics across different time scales. Secondly, we propose a Centered-progressive Subject Adversarial (C-SA) Mechanism to gradually narrow the metric distance between different subjects, thereby obtaining a unified and stable semantic representation. Thirdly, we design a New2Source Mapper to align the EEG distribution of new subjects with that of multiple known subjects. Finally, we adopt a keyframe-guided continuous semantic generation paradigm to drive the production of coherent and high-quality videos. Extensive experiments validate the competitive performance of our SAM-Net in cross-subject EEG-to-Video generation tasks, as well as its excellent performance in generation tasks involving new subjects.
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记录 #1 · 2026 · 最新观测版本
Cross-Subject EEG-to-Video Reconstruction and Beyond
Reconstructing video content from EEG (electroencephalogram) is a research task of significant scientific importance. However, due to inter-subject differences in physiological states and variations in signal acquisition configurations, this task faces the challenge of inconsistent cross-subject generation.To address this, we propose a Subject Adversarial and Mapping Network (SAM-Net). In SAM-Net, we first introduce a Hybrid Region-Temporal (HRT) Encoder to conduct inter-channel semantic interactions guided by brain regions and aggregate temporal semantics across different time scales. Secondly, we propose a Centered-progressive Subject Adversarial (C-SA) Mechanism to gradually narrow the metric distance between different subjects, thereby obtaining a unified and stable semantic representation. Thirdly, we design a New2Source Mapper to align the EEG distribution of new subjects with that of multiple known subjects. Finally, we adopt a keyframe-guided continuous semantic generation paradigm to drive the production of coherent and high-quality videos. Extensive experiments validate the competitive performance of our SAM-Net in cross-subject EEG-to-Video generation tasks, as well as its excellent performance in generation tasks involving new subjects.
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