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Semantic reconstruction of continuous language from non-invasive brain recordings
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Abstract
A brain-computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, non-invasive language decoders can only identify stimuli from among a small set of words or phrases. Here we introduce a non-invasive decoder that reconstructs continuous language from cortical semantic representations recorded using functional magnetic resonance imaging (fMRI). Given novel brain recordings, this decoder generates intelligible word sequences that recover the meaning of perceived speech, imagined speech and even silent videos, demonstrating that a single decoder can be applied to a range of tasks. We tested the decoder across cortex and found that continuous language can be separately decoded from multiple regions. As brain-computer interfaces should respect mental privacy, we tested whether successful decoding requires subject cooperation and found that subject cooperation is required both to train and to apply the decoder. Our findings demonstrate the viability of non-invasive language brain-computer interfaces.
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“Although these non-invasive techniques have facilitated decoding of limited vocabularies of imagined words, syllables and speech sounds75,76 and reconstruction of semantic content of speech68 (Fig. 1; Tang et al. 2023), they are not portable — with the exception of EEG — and low spatial or temporal resolution has hindered scaling decoding to larger vocabularies and longer speech segments (senten”
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“Semantics appear to be encoded in a distributed manner, across many cortical regions including prefrontal, speech and language association cortices67,68 (Fig. 1; Tang et al. 2023).”
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“The needs to handle novel applications never used before become evident by recent research [144] that shows the potential of ''mind-reading'' [145].”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [144]
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“For instance, the study in [144] shows the potential of leveraging language models as an autoregressive prior to generate novel sequences that can decode structured sequential information in the form of text from brain signals.”
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“Although the study of human imagination decoding shows human cooperation is required for the approach to work, this may not be a requisite in the future.”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [144]
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Metadata
- Authors
- Tang, J., LeBel, A., Jain, S., & Huth, A. G.
- Affiliations
- The University of Texas at Austin (US)
- Corresponding author
- Huth, Alexander G.
- Venue
- Nature Neuroscience
- Published
- 2023-05
- Volume
- 26
- Issue
- 5
- Pages
- 858-866
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC11304553
- OpenAlex
- W4367609927
- Cited by
- 442
- References
- 67
- Citations by year
- 2026: 105 · 2025: 140 · 2024: 133 · 2023: 60
- FWCI
- 65.59
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Neuroscience (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Alfred P. Sloan Foundation
- U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (1R01DC020088-001)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- semantic reconstruction, brain-computer interface, functional MRI, imagined speech, semantic representation, mental privacy
- MeSH terms
- Brain, Brain Mapping, Language, Magnetic Resonance Imaging, Semantics, Speech Perception, Brain-Computer Interfaces
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