RAG-based EEG-to-Text Translation Using Deep Learning and LLMs
The decoding of linguistic information from electroencephalography (EEG) signals remains an extremely challenging problem in brain-computer interface (BCI) research. In particular, sentence-level decoding from EEG is difficult due to the low signal-to-noise ratio of these recordings. Previous studies tackling this problem have typically failed to surpass random baseline performance unless teacher forcing is used during the inference phase. In this work, we propose a retrieval-augmented generation (RAG)-based sentence-level EEG-to-text decoding pipeline that combines an EEG encoder aligned with semantic sentence embeddings, a vector retrieval stage, and a large language model (LLM) to refi...
| Authors | Enrico Collautti, Xiaopeng Mao, Luca Tonin et al. |
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| Primary category | cs.AI |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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