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  3. Towards Decoding Individual Words From Non-invasive Brain Recordings.
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  3. Towards Decoding Individual Words From Non-invasive Brain Recordings.

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Towards decoding individual words from non-invasive brain recordings.

Stéphane d'Ascoli1, Corentin Bel2,3, Jérémy Rapin4

  • 1Meta AI, Paris, France. sdascoli@meta.com.

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|November 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers developed a deep learning pipeline to decode individual words from non-invasive brain recordings like electroencephalography (EEG) and magnetoencephalography (MEG). This advanced model significantly outperforms existing methods across various conditions, paving the way for non-invasive brain-computer interfaces.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Deep learning has advanced brain-computer interfaces (BCIs) for language decoding using invasive intracranial recordings.
  • Decoding natural language from non-invasive brain signals (EEG, MEG) remains a significant challenge.

Purpose of the Study:

  • To introduce and evaluate a deep learning pipeline for decoding individual words from electroencephalography (EEG) and magnetoencephalography (MEG) signals.
  • To assess the model's performance across diverse datasets, languages, and experimental conditions.

Main Methods:

  • A novel deep learning pipeline was developed for word decoding from EEG and MEG data.
  • The pipeline was validated on seven public and two newly collected datasets, totaling 723 participants and over five million words in three languages.
  • Performance was evaluated across different recording devices, tasks (reading vs. listening), and data volumes.

    • Main Results:

    • The proposed deep learning model consistently outperformed existing methods in word decoding accuracy across participants, devices, languages, and tasks.
    • The model demonstrated the ability to decode words not present in the training set (zero-shot decoding).
    • Decoding performance was influenced by the recording modality (MEG superior to EEG) and task (reading superior to listening), and improved with increased training data and testing signal averaging.

    Conclusions:

    • The developed deep learning pipeline represents a significant advancement in non-invasive natural language decoding from brain activity.
    • MEG and reading tasks offer more favorable conditions for decoding compared to EEG and listening.
    • Further research and data are crucial for refining non-invasive brain decoders for natural language applications.