Semantic reconstruction of continuous language from non-invasive brain recordings | Nature Neuroscience

Tang et al. show that continuous language can be decoded from functional MRI recordings to recover the meaning of perceived and imagined speech stimuli and silent videos and that this language decoding requires subject cooperation.

Subjects Functional magnetic resonance imaging Language Neural decoding 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 sequ

saved by

• Mackenzie Dion
• Amir
• Julian Quevedo
• Marley Xiong
• Lawrence H
• Swaminathan Gurumurthy
• Harrison Qian

related reading

• Brain2Qwerty — Decoding typed sentences from non-invasive brain activityfacebookresearch.github.io
• Toward accessible, real-time brain decoding: Introducing ENIGMA | Alljoined Blogalljoined.com
• Jean-Rémi KINGkingjr.github.io
• An underleveraged modality in neuroscience | Abhi Upadhyayaxu.sh
• From Brain Waves to Words: Brain2Qwerty Offers a New Path to Communication Without Surgeryai.meta.com
• Amphora — Emotion Intelligence for Generative AIamphorabrain.com
• A heart of firemarleyx.com
• AI to Interpret Your Mindjonathanxu.com
• Anna Ivanovaanna-ivanova.net
• Brain–computer interface - Wikipediaen.wikipedia.org
• Volitional control of individual neurons in the human brainpmc.ncbi.nlm.nih.gov
• Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priorsmedarc-ai.github.io