用视频生成脑电数据，助力情绪分析与脑机接口研究
A Dataset Generation Scheme Based on Video2EEG-SPGN-Diffusion for SEED-VD
- 结合自玩图网络与扩散模型生成个性化脑电信号
- 产出超1000组视频-脑电配对数据，采样率200Hz
- 适合脑机接口、情绪识别与多模态模型研究者
本文提出开源框架 Video2EEG-SPGN-Diffusion，基于 SEED-VD 数据集生成以视频为条件的多模态脑电数据。通过工程化对齐流程，实现视频与脑电信号的精准配对，支持具备脑电对齐能力的多模态大模型训练。采用融合自玩图网络（SPGN）与扩散模型的方法，生成62通道、200 Hz采样率的个性化脑电信号。作为主要贡献，本研究发布包含超过1000个样本的新数据集，每条样本均配有视频刺激、生成脑电及情绪标签，支持视频-脑电对齐，推动多模态研究发展。该框架在情绪分析、数据增强与脑机接口应用中具有重要研究价值。
原文摘要 · Abstract (English)
This paper introduces an open-source framework, Video2EEG-SPGN-Diffusion, that leverages the SEED-VD dataset to generate a multimodal dataset of EEG signals conditioned on video stimuli. Additionally, we disclose an engineering pipeline for aligning video and EEG data pairs, facilitating the training of multimodal large models with EEG alignment capabilities. Personalized EEG signals are generated using a self-play graph network (SPGN) integrated with a diffusion model. As a major contribution, we release a new dataset comprising over 1000 samples of SEED-VD video stimuli paired with generated 62-channel EEG signals at 200 Hz and emotion labels, enabling video-EEG alignment and advancing multimodal research. This framework offers novel tools for emotion analysis, data augmentation, and brain-computer interface applications, with substantial research and engineering significance.
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