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

2025-11-22 10:30:11
Mingyu Jeon, Hyobin Kim
arXiv_CL
           
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.

Abstract (translated)

URL

https://arxiv.org/abs/2511.21746

PDF

https://arxiv.org/pdf/2511.21746.pdf

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