# Decoding individual words from non-invasive brain recordings across 723 participants

arXiv: https://arxiv.org/abs/2412.17829 · HTML: https://mlsanity.com/paper/2412.17829/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2024.

## Built on by (most cited first, 13 of 13)

- [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)
- [Unlocking Non-Invasive Brain-to-Text](https://mlsanity.com/paper/2505.13446/) (2025)
- [EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding](https://mlsanity.com/paper/2506.19141/) (2025)
- [Dynadiff: Single-stage Decoding of Images from Continuously Evolving fMRI](https://mlsanity.com/paper/2505.14556/) (2025)
- [Mitigating Subject Dependency in EEG Decoding with Subject-Specific Low-Rank Adapters](https://mlsanity.com/paper/2510.08059/) (2025)
- [Elementary, My Dear Watson: Non-Invasive Neural Keyword Spotting in the LibriBrain Dataset](https://mlsanity.com/paper/2510.21038/) (2025)
- [Decoding Phone Pairs from MEG Signals Across Speech Modalities](https://mlsanity.com/paper/2505.15355/) (2025)
- [Scaling and context steer LLMs along the same computational path as the human brain](https://mlsanity.com/paper/2512.01591/) (2025)
- [From Minutes to Days: Scaling Intracranial Speech Decoding with Supervised Pretraining](https://mlsanity.com/paper/2512.15830/) (2025)
- [Learning aligned EEG representations with subject-specific encoders](https://mlsanity.com/paper/2606.16462/) (2026)
- [Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants](https://mlsanity.com/paper/2505.21304/) (2025)
- [MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data](https://mlsanity.com/paper/2602.18253/) (2026)

## Abstract (arXiv)

Deep learning has recently enabled the decoding of language from the neural activity of a few participants with electrodes implanted inside their brain. However, reliably decoding words from non-invasive recordings remains an open challenge. To tackle this issue, we introduce a novel deep learning pipeline to decode individual words from non-invasive electro- (EEG) and magneto-encephalography (MEG) signals. We train and evaluate our approach on an unprecedentedly large number of participants (723) exposed to five million words either written or spoken in English, French or Dutch. Our model outperforms existing methods consistently across participants, devices, languages, and tasks, and can decode words absent from the training set. Our analyses highlight the importance of the recording device and experimental protocol: MEG and reading are easier to decode than EEG and listening, respectively, and it is preferable to collect a large amount of data per participant than to repeat stimuli across a large number of participants. Furthermore, decoding performance consistently increases with the amount of (i) data used for training and (ii) data used for averaging during testing. Finally, single-word predictions show that our model effectively relies on word semantics but also captures syntactic and surface properties such as part-of-speech, word length and even individual letters, especially in the reading condition. Overall, our findings delineate the path and remaining challenges towards building non-invasive brain decoders for natural language.