mlsanityData to 7 Oct 2026

Brain-Driven Representation Learning Based on Diffusion Model

arXiv 2311.07925 ↗First posted 14 Nov 2023cs.CL cs.AIcited by 3

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Interpreting EEG signals linked to spoken language presents a complex challenge, given the data's intricate temporal and spatial attributes, as well as the various noise factors. Denoising diffusion probabilistic models (DDPMs), which have recently gained prominence in diverse areas for their capabilities in representation learning, are explored in our research as a means to address this issue. Using DDPMs in conjunction with a conditional autoencoder, our new approach considerably outperforms traditional machine learning algorithms and established baseline models in accuracy. Our results highlight the potential of DDPMs as a sophisticated computational method for the analysis of speech-related EEG signals. This could lead to significant advances in brain-computer interfaces tailored for spoken communication.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 5 of its 17 references that are in the map. First posted on arXiv 14 Nov 2023.

Builds on

5 of 17 references.

References · 5

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 616
  4. 2021FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interfacecited by 54
  5. 2021Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environmentcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 66
  2. 2025EEGDM: Learning EEG Representation with Latent Diffusion Modelcited by 1
  3. 2026Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG
  2. 2025EEGDM: EEG Representation Learning via Generative Diffusion Model
  3. 2024EEG-Based Speech Decoding: A Novel Approach Using Multi-Kernel Ensemble Diffusion Models
  4. 2026Addressing Limited Data in Auditory Attention Decoding with Diffusion Generative Models
  5. 2024Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models
  6. 2025Synthetic EEG Generation using Diffusion Models for Motor Imagery Tasks
  7. 2025D4PM: A Dual-branch Driven Denoising Diffusion Probabilistic Model with Joint Posterior Diffusion Sampling for EEG Artifacts Removal
  8. 2024Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model