ArticleNature communications2025
Towards decoding individual words from non-invasive brain recordings.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The Efficacy of Individualized Music Therapy in Patients With MCS: A Pilot Study on EEG.Neural plasticity · 2026Trial
- Speech Brain-Computer Interfaces: A New Pathway for Restoring Communication in Anarthria.Neuroscience bulletin · 2026Article
- Article
- From technological closed-loop to human-machine trust: a systematic review of ethical and communication challenges of brain-computer interface in elderly care.Frontiers in digital health · 2026Article
- Sensory sharpening and semantic prediction errors unify competing models of predictive processing in human speech comprehension.PLoS biology · 2026Article
- MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps and Noisy Sentences.Scientific data · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While deep learning has enabled the decoding of language from intracranial brain recordings, achieving this with non-invasive recordings remains an open challenge. We introduce a deep learning pipeline to decode individual words from electro- (EEG) and magneto-encephalography (MEG) signals. We evaluate our approach on seven public datasets and two datasets which we collect ourselves, amounting to a total of 723 participants reading or listening to five million words in three languages. 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, and decoding performance consistently increases with the amount of data used for training and for averaging during testing. Overall, our findings delineate the path and remaining challenges towards building non-invasive brain decoders for natural language.
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Registered trials
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