mlsanityData to 14 Oct 2026

A Dataset Generation Scheme Based on Video2EEG-SPGN-Diffusion for SEED-VD

arXiv 2509.05321 ↗First posted 30 Aug 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

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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References · 5

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Scaling Laws for Neural Language Modelscited by 6,586
  3. 2018An Empirical Model of Large-Batch Trainingcited by 281
  4. 2024Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Datacited by 13
  5. 2024Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Modelscited by 2

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  5. 2026Structure-Guided Diffusion Model for EEG-Based Visual Cognition Reconstruction
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  7. 2025MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition
  8. 2025DAMSDAN: Distribution-Aware Multi-Source Domain Adaptation Network for Cross-Domain EEG-based Emotion Recognition