Reconstructing visual experiences from brain activity evoked by natural movies. Bayesian reconstruction of natural images from human brain activity. Voxelwise encoding models show that cerebellar language representations are highly conceptual. The neural architecture of language: integrative modeling converges on predictive processing. In Advances in Neural Information Processing Systems 32 14928–14938 (NeurIPS, 2019). Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain). In Advances in Neural Information Processing Systems 31 6629–6638 (NeurIPS, 2018). Incorporating context into language encoding models for fMRI. The underpinnings of the BOLD functional magnetic resonance imaging signal. Decoding imagined and spoken phrases from non-invasive neural (MEG) signals. Toward a universal decoder of linguistic meaning from brain activation. Predicting human brain activity associated with the meanings of nouns. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Brains and algorithms partially converge in natural language processing. Electrophysiological correlates of semantic dissimilarity reflect the comprehension of natural, narrative speech. The hierarchical cortical organization of human speech processing. Natural speech reveals the semantic maps that tile human cerebral cortex. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. High-performance brain-to-text communication via handwriting. Reconstructing speech from human auditory cortex. Speech synthesis from neural decoding of spoken sentences. Our findings demonstrate the viability of non-invasive language brain–computer interfaces.Īnumanchipalli, G. 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. We tested the decoder across cortex and found that continuous language can be separately decoded from multiple regions. 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. Here we introduce a non-invasive decoder that reconstructs continuous language from cortical semantic representations recorded using functional magnetic resonance imaging (fMRI). Currently, however, non-invasive language decoders can only identify stimuli from among a small set of words or phrases. A brain–computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications.

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