EEG-to-Text Decoding

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13 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Deep LLM layers expose passage-level context in EEG during silent reading
Oct 16, 2026Ingo Marquardt, Anthilia Alchanat, Priyanka JainEEG Decoding EEG-to-Text Decoding
nubrain
Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech
Oct 14, 2026Shahryar G. Namdari, Francesco Iacomi, Riccardo Barbieri +1EEG Decoding EEG-to-Text Decoding
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, 20133, Italy
Decoding silent reading from non-invasive EEG
Aug 20, 2026Ingo Marquardt, Anthilia Alchanat, Priyanka JainEEG Decoding EEG-to-Text Decoding
nubrain
Joint Text-Audio Alignment for EEG-to-Text Decoding in Chinese Speech Production and Perception
Jul 28, 2026Tian Zheng, Xurong Xie, Xinxin Zhu +2Cross-Modal Representation Learning EEG Decoding
University of the Chinese Academy of Sciences · Institute of Software, Chinese Academy of Sciences · Beijing Language and Culture University
Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark
Jul 21, 2026Zihan Zhang, Yu Bao, Xiao Ding +2Electroencephalography EEG Decoding
Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology · Shanghai Innovation Institute · Shanghai Innovation Institute, Shanghai, China +3
Decoding EEG Signals to Explore Next-Word Predictability in the Human Brain
Jul 17, 2026Boi Mai Quach, Binh T. Nguyen, Cathal Gurrin +1EEG-to-Text Decoding Computational Psycholinguistics
School of Computing, ML-Labs, Department of Computer Science, School of Computing, ADAPT Centre, Dublin City University, Ireland · VNUHCM – University of Science
A 1000-hour EEG-EMG-audio dataset of Japanese speech production
May 31, 2026Motoshige Sato, Ilya Horiguchi, Masakazu Inoue +6Electroencephalography EEG-to-Text Decoding
Araya Inc., Tokyo, Japan
SYNAPSE: Neuro-Symbolic Visual Thought-to-Text Decoding via Topological Semantic Denoising
May 27, 2026Akshaj Murhekar, Abhijit MishraLanguage Model Decoding EEG Decoding
School of Information University of Texas at Austin
RAG-based EEG-to-Text Translation Using Deep Learning and LLMs
May 17, 2026Enrico Collautti, Xiaopeng Mao, Luca Tonin +2Retrieval-Augmented Generation Brain-Computer Interfaces
IAS-LAB, Department of Information Engineering, University of Padova, 35122 Padova (PD), Italy · Department of Health Technology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark · Padova Neuroscience Center, 35131 Padova (PD), Italy.
Brain-CLIPLM: Semantic Compression for EEG-to-Text Decoding
Mar 23, 2026Xiaoli Yang, Huiyuan Tian, Yurui Li +3EEG-to-Text Decoding EEG Decoding
College of Computer Science and Technology, Zhejiang University, Hangzhou, China · State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
CIPHER: Conformer-based Inference of Phonemes from High-density EEG
Mar 22, 2026Varshith MadishettyEEG-to-Text Decoding Conformer
SENSE: Efficient EEG-to-Text via Privacy-Preserving Semantic Retrieval
Mar 17, 2026Akshaj Murhekar, Christina Liu, Abhijit Mishra +2EEG-to-Text Decoding EEG Decoding
The University of Texas at Austin School of Information Austin, Texas, USA · Institute of Applied Computer Science Łódź University of Technology Poland
Hierarchic-EEG2Text: Assessing EEG-To-Text Decoding across Hierarchical Abstraction Levels
Feb 24, 2026Anupam Sharma, Harish Katti, Prajwal Singh +2EEG Decoding EEG-to-Text Decoding
IIT Gandhinagar Gandhinagar, Gujarat, India · NIMH, NIH USA

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Deep LLM layers expose passage-level context in EEG during silent reading

Ingo Marquardt, Anthilia Alchanat, Priyanka Jain

nubrain

EEG Decoding EEG-to-Text Decoding

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In a previous report we showed that a decoder can recover open-vocabulary word-level information from 19-channel EEG recorded during silent reading, by aligning short EEG windows with hidden-state embeddings of the presented word taken from a large language model (LLM). That analysis contrasted two of the model's 33 layers: the context-free input embedding and one mid-depth layer. Here we ask how EEG-decodable structure is distributed over the full depth of the language model. We trained one decoder per layer of Llama-3.1-8B, with and without a causal transformer over the sequence of EEG windows. We distinguish word-level decodability from a context-tracking component. Word-level decodability peaks in early layers and declines steadily towards the last layer. In contrast, the context-tracking component of the retrieval gain rises with depth, both with and without a causal transformer. Unexpectedly, a decoder that sees a single 0.5-s window at a time tracks the text passage too. Two new controls dissect this component. A target-geometry control, a synthetic predictor made of the true target plus noise calibrated to each decoder's word-level accuracy, shows that context tracking is not a by-product of word decoding and target geometry. A control that permutes predictions across runs from either the same or from different sessions shows that most of the observed context-tracking is not caused by an EEG recording session signature. Instead, context tracking mostly follows the text. A single EEG window therefore carries passage-level information that a contextual target can absorb. The results locate word identity in the early layers of the language model, discourse state in the deeper layers, and make the choice of target layer a design dial for EEG language decoders.