# DELTA: Language Diffusion-based EEG-to-Text Architecture

arXiv: https://arxiv.org/abs/2511.21746 · HTML: https://mlsanity.com/paper/2511.21746/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Builds on (most cited first, 17 of 17)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Large Language Diffusion Models](https://mlsanity.com/paper/2502.09992/) (2025)
- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification](https://mlsanity.com/paper/2112.02690/) (2021)
- [ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation](https://mlsanity.com/paper/1912.00903/) (2019)
- [DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation](https://mlsanity.com/paper/2309.14030/) (2023)
- [Are EEG-to-Text Models Working?](https://mlsanity.com/paper/2405.06459/) (2024)
- [Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)](https://mlsanity.com/paper/2410.07507/) (2024)
- [MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification](https://mlsanity.com/paper/2409.04104/) (2024)

## Built on by (most cited first, 1 of 1)

- [Brain-to-Language Decoding: Tasks, Signals, Methods, Evaluation, Practical Use and Beyond](https://mlsanity.com/paper/2609.27650/) (2026)

## Abstract (arXiv)

Electroencephalogram (EEG)-to-text remains challenging due to high-dimensional noise, subject variability, and error accumulation in autoregressive decoding. We introduce DELTA, which pairs a Residual Vector Quantization (RVQ) EEG tokenizer with a masked language diffusion model (LLaDA). RVQ discretizes continuous EEG into multi-layer tokens to reduce noise and individual differences, while LLaDA reconstructs sentences via non-sequential denoising. On ZuCo, DELTA improves semantic alignment by up to 5.37 points over autoregressive baselines, achieving BLEU-1 21.9 and ROUGE-1 F 17.2 under word-level conditions. These results enable reliable text generation from small EEG-text datasets and point toward scalable multimodal EEG-language models.