# Brain-to-Language Decoding: Tasks, Signals, Methods, Evaluation, Practical Use and Beyond

arXiv: https://arxiv.org/abs/2609.27650 · HTML: https://mlsanity.com/paper/2609.27650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 495 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 30 of 59)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Mean Flows for One-step Generative Modeling](https://mlsanity.com/paper/2505.13447/) (2025)
- [Transformer-based Spatial-Temporal Feature Learning for EEG Decoding](https://mlsanity.com/paper/2106.11170/) (2021)
- [Mousai: Text-to-Music Generation with Long-Context Latent Diffusion](https://mlsanity.com/paper/2301.11757/) (2023)
- [Neuro-GPT: Towards A Foundation Model for EEG](https://mlsanity.com/paper/2311.03764/) (2023)
- [BrainBERT: Self-supervised representation learning for intracranial recordings](https://mlsanity.com/paper/2302.14367/) (2023)
- [BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals](https://mlsanity.com/paper/2505.18185/) (2025)
- [Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)](https://mlsanity.com/paper/2307.10246/) (2023)
- [EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification](https://mlsanity.com/paper/2407.20254/) (2024)
- [EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer](https://mlsanity.com/paper/2405.02165/) (2024)
- [The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset](https://mlsanity.com/paper/2506.10165/) (2025)
- [Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli](https://mlsanity.com/paper/2411.08343/) (2024)
- [FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model](https://mlsanity.com/paper/2502.06438/) (2025)
- [MAD: Multi-Alignment MEG-to-Text Decoding](https://mlsanity.com/paper/2406.01512/) (2024)
- [Unlocking Non-Invasive Brain-to-Text](https://mlsanity.com/paper/2505.13446/) (2025)
- [Neural Codecs as Biosignal Tokenizers](https://mlsanity.com/paper/2510.09095/) (2025)
- [NeuSpeech: Decode Neural signal as Speech](https://mlsanity.com/paper/2403.01748/) (2024)
- [BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decoding](https://mlsanity.com/paper/2409.00121/) (2024)
- [Brain-to-Text Benchmark '24: Lessons Learned](https://mlsanity.com/paper/2412.17227/) (2024)
- [OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models](https://mlsanity.com/paper/2606.00815/) (2026)
- [NeuGPT: Unified multi-modal Neural GPT](https://mlsanity.com/paper/2410.20916/) (2024)
- [MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training](https://mlsanity.com/paper/2602.02494/) (2026)
- [Time-Masked Transformers with Lightweight Test-Time Adaptation for Neural Speech Decoding](https://mlsanity.com/paper/2507.02800/) (2025)
- [Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation](https://mlsanity.com/paper/2505.17099/) (2025)
- [LibriBrain100: One Hundred Hours of Broad and Deep MEG Data for Neural Speech Decoding at Scale](https://mlsanity.com/paper/2608.25204/) (2026)
- [Pretraining Large Brain Language Model for Active BCI: Silent Speech](https://mlsanity.com/paper/2504.21214/) (2025)
- [EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG](https://mlsanity.com/paper/2506.00854/) (2025)
- [On Creating A Brain-To-Text Decoder](https://mlsanity.com/paper/2501.06326/) (2025)
- [BP-GPT: Auditory Neural Decoding Using fMRI-prompted LLM](https://mlsanity.com/paper/2502.15172/) (2025)
- [Bridging Brain Signals and Language: A Deep Learning Approach to EEG-to-Text Decoding](https://mlsanity.com/paper/2502.17465/) (2025)

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

- [Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text](https://mlsanity.com/paper/2609.40359/) (2026)

## Abstract (arXiv)

Brain-to-language decoding translates neural activity associated with language production, internal speech and perception into linguistic or expressive outputs. It offers a route to restoring communication after speech loss and a means of studying how the brain represents language. Advances in neural recording and representation learning have expanded the field from constrained recognition and acoustic reconstruction to text generation, streaming personalised speech and facial animation. This survey synthesises these developments across invasive and non-invasive measurements, drawing on a search without a lower year limit and source-led updates through September 2026. We connect Articulated, Inner and Perceived tasks to the neural populations they engage, the representations available to decoders and the outputs those representations can support. We examine model development, public resources and the evolution of evaluation, and compare published performance and communication costs within their reported protocols. The synthesis identifies complementary routes to progress: phonetic, acoustic and semantic targets preserve different aspects of a message; shared representations support reuse across recording conditions and tasks; and online communication increasingly depends on calibration, feedback and user control alongside decoding accuracy. Shared benchmarks enable algorithmic comparisons, while longitudinal studies reveal the demands of sustained use. We discuss these developments and their remaining limitations, then outline a prospective five-level trajectory from commands and language to meaning, scenarios and bidirectional cognitive exchange