# Brain-to-Text Decoding: A Non-invasive Approach via Typing

arXiv: https://arxiv.org/abs/2502.17480 · HTML: https://mlsanity.com/paper/2502.17480/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 52 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)
- [From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production](https://mlsanity.com/paper/2502.07429/) (2025)

## Built on by (most cited first, 22 of 22)

- [NeuralBench: A Unifying Framework to Benchmark NeuroAI Models](https://mlsanity.com/paper/2605.08495/) (2026)
- [Unlocking Non-Invasive Brain-to-Text](https://mlsanity.com/paper/2505.13446/) (2025)
- [EEG-CLIP : Learning EEG representations from natural language descriptions](https://mlsanity.com/paper/2503.16531/) (2025)
- [Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation](https://mlsanity.com/paper/2505.17099/) (2025)
- [Foundation Models for Cross-Domain EEG Analysis Application: A Survey](https://mlsanity.com/paper/2508.15716/) (2025)
- [Mitigating Subject Dependency in EEG Decoding with Subject-Specific Low-Rank Adapters](https://mlsanity.com/paper/2510.08059/) (2025)
- [Accurate Decoding of Natural Sentences from Non-Invasive Brain Recordings](https://mlsanity.com/paper/2608.18114/) (2026)
- [Decoding Phone Pairs from MEG Signals Across Speech Modalities](https://mlsanity.com/paper/2505.15355/) (2025)
- [SENSE: Efficient EEG-to-Text via Privacy-Preserving Semantic Retrieval](https://mlsanity.com/paper/2603.17109/) (2026)
- [Bridging Brain with Foundation Models through Self-Supervised Learning](https://mlsanity.com/paper/2506.16009/) (2025)
- [Decoding Neural Emotion Patterns through Large Language Model Embeddings](https://mlsanity.com/paper/2508.09337/) (2025)
- [Transfer Learning from ImageNet for MEG-Based Decoding of Imagined Speech](https://mlsanity.com/paper/2601.15909/) (2026)
- [EDAPT: Towards Calibration-Free BCIs with Continual Online Adaptation](https://mlsanity.com/paper/2508.10474/) (2025)
- [Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?](https://mlsanity.com/paper/2509.10875/) (2025)
- [MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data](https://mlsanity.com/paper/2602.18253/) (2026)
- [DANCE: Detect and Classify Events in EEG](https://mlsanity.com/paper/2605.10688/) (2026)
- [SYNAPSE: Neuro-Symbolic Visual Thought-to-Text Decoding via Topological Semantic Denoising](https://mlsanity.com/paper/2605.27790/) (2026)
- [Brain-Prompt Injection: A Route-Safety Audit for BCI-LLM Agents](https://mlsanity.com/paper/2606.09315/) (2026)
- [Myovox: Reading Speech from the Muscles of the Face](https://mlsanity.com/paper/2609.17548/) (2026)
- [Integrating Language Models into Listened and Imagined Speech Decoding from MEG](https://mlsanity.com/paper/2609.31997/) (2026)
- [From Neurons to Conversation: Speech Brain-Computer Interfaces](https://mlsanity.com/paper/2609.36736/) (2026)
- [Shared Geometry As A Rosetta Stone: Cross-Modal Alignment Without Paired Data](https://mlsanity.com/paper/2610.09411/) (2026)

## Abstract (arXiv)

Modern neuroprostheses can now restore communication in patients who have lost the ability to speak or move. However, these invasive devices entail risks inherent to neurosurgery. Here, we introduce a non-invasive method to decode the production of sentences from brain activity and demonstrate its efficacy in a cohort of 35 healthy volunteers. For this, we present Brain2Qwerty, a new deep learning architecture trained to decode sentences from either electro- (EEG) or magneto-encephalography (MEG), while participants typed briefly memorized sentences on a QWERTY keyboard. With MEG, Brain2Qwerty reaches, on average, a character-error-rate (CER) of 32% and substantially outperforms EEG (CER: 67%). For the best participants, the model achieves a CER of 19%, and can perfectly decode a variety of sentences outside of the training set. While error analyses suggest that decoding depends on motor processes, the analysis of typographical errors suggests that it also involves higher-level cognitive factors. Overall, these results narrow the gap between invasive and non-invasive methods and thus open the path for developing safe brain-computer interfaces for non-communicating patients.