# Unlocking Non-Invasive Brain-to-Text

arXiv: https://arxiv.org/abs/2505.13446 · HTML: https://mlsanity.com/paper/2505.13446/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data](https://mlsanity.com/paper/2101.12037/) (2021)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification](https://mlsanity.com/paper/2112.02690/) (2021)
- [Are EEG-to-Text Models Working?](https://mlsanity.com/paper/2405.06459/) (2024)
- [Brain-to-Text Decoding: A Non-invasive Approach via Typing](https://mlsanity.com/paper/2502.17480/) (2025)
- [The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning](https://mlsanity.com/paper/2406.04328/) (2024)
- [emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography](https://mlsanity.com/paper/2410.20081/) (2024)
- [MAD: Multi-Alignment MEG-to-Text Decoding](https://mlsanity.com/paper/2406.01512/) (2024)
- [Decoding individual words from non-invasive brain recordings across 723 participants](https://mlsanity.com/paper/2412.17829/) (2024)
- [NeuSpeech: Decode Neural signal as Speech](https://mlsanity.com/paper/2403.01748/) (2024)
- [NeuGPT: Unified multi-modal Neural GPT](https://mlsanity.com/paper/2410.20916/) (2024)
- [Resolving Domain Shift For Representations Of Speech In Non-Invasive Brain Recordings](https://mlsanity.com/paper/2410.19986/) (2024)
- [Non-invasive Neural Decoding in Source Reconstructed Brain Space](https://mlsanity.com/paper/2410.19838/) (2024)

## Built on by (most cited first, 15 of 15)

- [LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale](https://mlsanity.com/paper/2506.02098/) (2025)
- [The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset](https://mlsanity.com/paper/2506.10165/) (2025)
- [MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training](https://mlsanity.com/paper/2602.02494/) (2026)
- [Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies](https://mlsanity.com/paper/2510.22095/) (2025)
- [LibriBrain100: One Hundred Hours of Broad and Deep MEG Data for Neural Speech Decoding at Scale](https://mlsanity.com/paper/2608.25204/) (2026)
- [MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification](https://mlsanity.com/paper/2512.01443/) (2025)
- [Elementary, My Dear Watson: Non-Invasive Neural Keyword Spotting in the LibriBrain Dataset](https://mlsanity.com/paper/2510.21038/) (2025)
- [What Are We Actually Decoding? Source Attribution for Non-Invasive Brain-to-Language Retrieval](https://mlsanity.com/paper/2605.24524/) (2026)
- [A Common Measure of Communication for Speech Brain-Computer Interfaces](https://mlsanity.com/paper/2609.02887/) (2026)
- [The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100](https://mlsanity.com/paper/2609.03231/) (2026)
- [From Minutes to Days: Scaling Intracranial Speech Decoding with Supervised Pretraining](https://mlsanity.com/paper/2512.15830/) (2025)
- [Brain-to-Language Decoding: Tasks, Signals, Methods, Evaluation, Practical Use and Beyond](https://mlsanity.com/paper/2609.27650/) (2026)
- [MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data](https://mlsanity.com/paper/2602.18253/) (2026)
- [MeGA-MP: Metric Graph Advection Message Passing -- A Physics-Informed Message Passing Operator for Advection-Dominated Metric Graphs](https://mlsanity.com/paper/2607.05167/) (2026)
- [Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text](https://mlsanity.com/paper/2609.40359/) (2026)

## Abstract (arXiv)

Despite major advances in surgical brain-to-text (B2T), i.e. transcribing speech from invasive brain recordings, non-invasive alternatives have yet to surpass even chance on standard metrics. This remains a barrier to building a non-invasive brain-computer interface (BCI) capable of restoring communication in paralysed individuals without surgery. Here, we present the first non-invasive B2T result that significantly exceeds these critical baselines, raising BLEU by 1.4-2.6× over prior work. This result is driven by three contributions: (1) we extend recent word-classification models with LLM-based rescoring, transforming single-word predictors into closed-vocabulary B2T systems; (2) we introduce a predictive in-filling approach to handle out-of-vocabulary (OOV) words, substantially expanding the effective vocabulary; and (3) we demonstrate, for the first time, how to scale non-invasive B2T models across datasets, unlocking deep learning at scale and improving accuracy by 2.1-2.3×. Through these contributions, we offer new insights into the roles of data quality and vocabulary size. Together, our results remove a major obstacle to realising practical non-invasive B2T systems.