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

Mingyu Jeon, Hyobin Kim
11/22/2025
cs.CL

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Abstract

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.

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Cite this paper

@article{jeon2025delta,
  title  = {DELTA: Language Diffusion-based EEG-to-Text Architecture},
  author = {Mingyu Jeon and Hyobin Kim},
  year   = {2025},
  eprint = {2511.21746},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.21746v1}
}

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