mlsanityData to 14 Oct 2026

Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG

arXiv 2307.14389 ↗First posted 26 Jul 2023eess.AS cs.CL cs.HCcited by 17

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Decoding EEG signals for imagined speech is a challenging task due to the high-dimensional nature of the data and low signal-to-noise ratio. In recent years, denoising diffusion probabilistic models (DDPMs) have emerged as promising approaches for representation learning in various domains. Our study proposes a novel method for decoding EEG signals for imagined speech using DDPMs and a conditional autoencoder named Diff-E. Results indicate that Diff-E significantly improves the accuracy of decoding EEG signals for imagined speech compared to traditional machine learning techniques and baseline models. Our findings suggest that DDPMs can be an effective tool for EEG signal decoding, with potential implications for the development of brain-computer interfaces that enable communication through imagined speech.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 14 of its 35 references that are in the map. First posted on arXiv 26 Jul 2023.

Builds on

14 of 35 references.

References · 14

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  5. 2015Cyclical Learning Rates for Training Neural Networkscited by 629
  6. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
  7. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 582
  8. 2021Diffusion Autoencoders: Toward a Meaningful and Decodable Representationcited by 449
  9. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 337
  10. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 198
  11. 2021Diff-TTS: A Denoising Diffusion Model for Text-to-Speechcited by 157
  12. 2022Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Modelscited by 54
2 more
  1. 2023Towards Voice Reconstruction from EEG during Imagined Speechcited by 26
  2. 2021EEG-Transformer: Self-attention from Transformer Architecture for Decoding EEG of Imagined Speechcited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 7; 2026: 1.

17 papers.

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  1. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 67
  2. 2024RespDiff: An End-to-End Multi-scale RNN Diffusion Model for Respiratory Waveform Estimation from PPG Signalscited by 6
  3. 2024Diff-ETS: Learning a Diffusion Probabilistic Model for Electromyography-to-Speech Conversioncited by 4
  4. 2023D3A-TS: Denoising-Driven Data Augmentation in Time Seriescited by 3
  5. 2025Towards Dynamic Neural Communication and Speech Neuroprosthesis Based on Viseme Decodingcited by 2
  6. 2025Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal Transformercited by 2
  7. 2024Towards Unified Neural Decoding of Perceived, Spoken and Imagined Speech from EEG Signalscited by 1
  8. 2025EEGDM: Learning EEG Representation with Latent Diffusion Modelcited by 1
  9. 2025MultiDiffNet: A Multi-Objective Diffusion Framework for Generalizable Brain Decodingcited by 1
  10. 2023Neural Speech Embeddings for Speech Synthesis Based on Deep Generative Networkscited by 0
  11. 2024An Audio-textual Diffusion Model For Converting Speech Signals Into Ultrasound Tongue Imaging Datacited by 0
  12. 2024Towards Scalable Handwriting Communication via EEG Decoding and Latent Embedding Integrationcited by 0
5 more
  1. 2024Dynamic Neural Communication: Convergence of Computer Vision and Brain-Computer Interfacecited by 0
  2. 2025Reconstructing Unseen Sentences from Speech-related Biosignals for Open-vocabulary Neural Communicationcited by 0
  3. 2025Distinct Theta Synchrony across Speech Modes: Perceived, Spoken, Whispered, and Imaginedcited by 0
  4. 2025Lightweight Diffusion-based Framework for Online Imagined Speech Decoding in Aphasiacited by 0
  5. 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. 2023Brain-Driven Representation Learning Based on Diffusion Model
  2. 2024EEG-Based Speech Decoding: A Novel Approach Using Multi-Kernel Ensemble Diffusion Models
  3. 2026Addressing Limited Data in Auditory Attention Decoding with Diffusion Generative Models
  4. 2025EEGDM: EEG Representation Learning via Generative Diffusion Model
  5. 2025A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond
  6. 2024Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models
  7. 2025Synthetic EEG Generation using Diffusion Models for Motor Imagery Tasks
  8. 2025LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignment