ArticleNature neuroscience2023
Semantic reconstruction of continuous language from non-invasive brain recordings.
Article in Nature neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers, 2 of them syntheses that pooled it.
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Who cites it
127 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for brain-to-speech decoding in paralysis: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Pooled it
- Semantic encoding of trauma memories in the hippocampus among individuals with PTSD.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Trial
- Noninvasive decoding of typed sentences from human brain activity.Nature neuroscience · 2026Article
- Reconciling global collaboration and data sovereignty in neuroscience.Nature neuroscience · 2026Review
- Medial temporal default mode network selectively encodes autobiographical visual imagery.Science advances · 2026Article
- Integration of functional magnetic resonance imaging and artificial intelligence in Alzheimer's disease.Neural regeneration research · 2026Article
- Cognitive Sovereignty: An AI-Aware Governance Framework for Neural Data Threats, Autonomous Cyber Defense, and Identity-Aware Security in Neurotechnology Systems.Neuroinformatics · 2026Article
- Ongoing thoughts at rest reflect functional brain organization and behaviour.Nature human behaviour · 2026Article
- A cortical gradient in area 55b links language and somato-cognitive action networks.Research square · 2026Article
- Cedalion tutorial: a Python-based framework for comprehensive analysis of multimodal fNIRS and DOT from the lab to the everyday world.Neurophotonics · 2026Article
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- Probabilistic Forecasting and Information-Theoretic Analysis of Multivariate fMRI Dynamics.Entropy (Basel, Switzerland) · 2026Article
- Glymphatic system impairment in neurological disorders: potential mechanisms and therapeutic targets.Molecular biomedicine · 2026Review
- Mapping knowledge structure and emerging trends in non-invasive brain-computer interface for stroke rehabilitation.IBRO neuroscience reports · 2026Article
- Imagined Speech Brain-Computer Interface: A Task-Oriented Review of Neural Decoding.Sensors (Basel, Switzerland) · 2026Review
- Decoding Mandarin Action Verbs from EEG Using a Dual-LSTM Network: Towards Practical Assistive Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2026Article
- A large-scale fMRI dataset for vision-language semantic association.Scientific data · 2026Article
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- Decoding concept representations in aphasia after stroke.bioRxiv : the preprint server for biology · 2026Article
67 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.