https://www.biorxiv.org/content/10.1101/2022.09.29.509744v1 Skip to main content bioRxiv * Home * About * Submit * ALERTS / RSS Search for this keyword [ ] [Search] Advanced Search New Results Semantic reconstruction of continuous language from non-invasive brain recordings Jerry Tang, Amanda LeBel, Shailee Jain, Alexander G Huth doi: https://doi.org/10.1101/2022.09.29.509744 Jerry Tang The University of Texas at Austin * Find this author on Google Scholar * Find this author on PubMed * Search for this author on this site Amanda LeBel The University of Texas at Austin * Find this author on Google Scholar * Find this author on PubMed * Search for this author on this site Shailee Jain The University of Texas at Austin * Find this author on Google Scholar * Find this author on PubMed * Search for this author on this site Alexander G Huth The University of Texas at Austin * Find this author on Google Scholar * Find this author on PubMed * Search for this author on this site * For correspondence: huth@cs.utexas.edu * Abstract * Info/History * Metrics * Preview PDF Loading Abstract A brain-computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, decoders that reconstruct continuous language use invasive recordings from surgically implanted electrodes, while decoders that use non-invasive recordings can only identify stimuli from among a small set of letters, words, or phrases. Here we introduce a non-invasive decoder that reconstructs continuous natural language from cortical representations of semantic meaning 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 language decoder can be applied to a range of semantic tasks. To study how language is represented across the brain, we tested the decoder on different cortical networks, and found that natural language can be separately decoded from multiple cortical networks in each hemisphere. 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 study demonstrates that continuous language can be decoded from non-invasive brain recordings, enabling future multipurpose brain-computer interfaces. Competing Interest Statement A.G.H. and J.T. have a provisional patent application related to this work. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. View the discussion thread. Back to top PreviousNext Posted September 29, 2022. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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[Send Message] Share Semantic reconstruction of continuous language from non-invasive brain recordings Jerry Tang, Amanda LeBel, Shailee Jain, Alexander G Huth bioRxiv 2022.09.29.509744; doi: https://doi.org/10.1101/ 2022.09.29.509744 Share This Article: [https://www.biorxiv.org/content/10.1101/2022.09.29] Copy Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo Citation Tools Semantic reconstruction of continuous language from non-invasive brain recordings Jerry Tang, Amanda LeBel, Shailee Jain, Alexander G Huth bioRxiv 2022.09.29.509744; doi: https://doi.org/10.1101/ 2022.09.29.509744 Citation Manager Formats * BibTeX * Bookends * EasyBib * EndNote (tagged) * EndNote 8 (xml) * Medlars * Mendeley * Papers * RefWorks Tagged * Ref Manager * RIS * Zotero * Tweet Widget * Facebook Like * Google Plus One Subject Area * Neuroscience Subject Areas All Articles * Animal Behavior and Cognition (3808) * Biochemistry (8094) * Bioengineering (5883) * Bioinformatics (21836) * Biophysics (10908) * Cancer Biology (8471) * Cell Biology (12295) * Clinical Trials (138) * Developmental Biology (6975) * Ecology (10672) * Epidemiology (2065) * Evolutionary Biology (14255) * Genetics (9897) * Genomics (13303) * Immunology (8428) * Microbiology (20635) * Molecular Biology (8128) * Neuroscience (44222) * Paleontology (329) * Pathology (1323) * Pharmacology and Toxicology (2325) * Physiology (3474) * Plant Biology (7439) * Scientific Communication and Education (1348) * Synthetic Biology (2067) * Systems Biology (5663) * Zoology (1161)