mlsanityData to 7 Oct 2026

Geometry- and Relation-Aware Diffusion for EEG Super-Resolution

arXiv 2602.02238 ↗First posted 2 Feb 2026cs.LG cs.AIcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Recent electroencephalography (EEG) spatial super-resolution (SR) methods, while showing improved quality by either directly predicting missing signals from visible channels or adapting latent diffusion-based generative modeling to temporal data, often lack awareness of physiological spatial structure, thereby constraining spatial generation performance. To address this issue, we introduce TopoDiff, a geometry- and relation-aware diffusion model for EEG spatial super-resolution. Inspired by how human experts interpret spatial EEG patterns, TopoDiff incorporates topology-aware image embeddings derived from EEG topographic representations to provide global geometric context for spatial generation, together with a dynamic channel-relation graph that encodes inter-electrode relationships and evolves with temporal dynamics. This design yields a spatially grounded EEG spatial super-resolution framework with consistent performance improvements. Across multiple EEG datasets spanning diverse applications, including SEED/SEED-IV for emotion recognition, PhysioNet motor imagery (MI/MM), and TUSZ for seizure detection, our method achieves substantial gains in generation fidelity and leads to notable improvements in downstream EEG task performance.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 29 of its 61 references that are in the map. First posted on arXiv 2 Feb 2026.

Builds on

29 of 61 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Step-Aware Residual-Guided Diffusion for EEG Spatial Super-Resolution22 Oct
  2. 2024Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning3 Jul
  3. 2023Data Augmentation for Seizure Prediction with Generative Diffusion Model14 Jun

Other references · 26

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  7. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  8. 2022Scalable Diffusion Models with Transformerscited by 5,931
  9. 2022Classifier-Free Diffusion Guidancecited by 5,717
  10. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,094
  11. 2018Neural Ordinary Differential Equationscited by 3,733
  12. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,536
14 more
  1. 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformercited by 2,198
  2. 2025DINOv3cited by 1,297
  3. 2023Stochastic Interpolants: A Unifying Framework for Flows and Diffusionscited by 897
  4. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 616
  5. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 577
  6. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 332
  7. 2025DeepSeek-OCR: Contexts Optical Compressioncited by 174
  8. 2019Deep learning-based electroencephalography analysis: a systematic reviewcited by 155
  9. 2018The Temple University Hospital Seizure Detection Corpuscited by 80
  10. 2023Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Modelscited by 24
  11. 2023ESTformer: Transformer utilising spatiotemporal dependencies for electroencephalogram super-resolutioncited by 8
  12. 2025Deep EEG Super-Resolution: Upsampling EEG Spatial Resolution with Generative Adversarial Networkscited by 8
  13. 2025EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networkscited by 8
  14. 2024RDPI: A Refine Diffusion Probability Generation Method for Spatiotemporal Data Imputationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Coordinate-Queryable Neural Field Reconstruction for EEG Spatial Super-Resolution with Unseen-Electrode Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026TGSD: Topology-Guided State-Space Diffusion Framework for EEG Spatial Super-Resolution
  2. 2026Structure-Guided Diffusion Model for EEG-Based Visual Cognition Reconstruction
  3. 2025Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning
  4. 2025EEG-Driven Image Reconstruction with Saliency-Guided Diffusion Models
  5. 2025Brain-Gen: Towards Interpreting Neural Signals for Stimulus Reconstruction Using Transformers and Latent Diffusion Models
  6. 2026EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience
  7. 2026Sensor Geometry as a Flow-Matching Prior for Multi-Channel Brain Signals
  8. 2024Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models