ArticleNature communications2019
Real-time decoding of question-and-answer speech dialogue using human cortical activity.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 1 of them a synthesis that pooled it.
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Who cites it
93 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for brain-to-speech decoding in paralysis: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Real-time detection of spoken speech from unlabeled ECoG signals: a pilot study with an ALS participant.Journal of neural engineering · 2025Trial
- Speech-related dorsal motor cortex activity does not interfere with iBCI cursor control.Journal of neural engineering · 2020Trial
- Decoding and Characterizing the Intracranial Representation of Semantic Information.bioRxiv : the preprint server for biology · 2026Article
- Minimally invasive implantation of scalable high-density cortical microelectrode arrays for multimodal neural decoding and stimulation.Nature biomedical engineering · 2026Article
- Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants.Nature biomedical engineering · 2026Article
- A multi-region flexible neural interface for behavioral state decoding in freely moving mice.Microsystems & nanoengineering · 2026Article
- Spatiotemporal Dynamics in Prespeech Semantic Category Decoding: An Intracranial EEG Study.eNeuro · 2026Article
- Decoding intended speech with an intracortical brain-computer interface in a person with long-standing anarthria and locked-in syndrome.Cell reports · 2026Article
- Stable speech BCI performance during slow progression of ALS: A longitudinal ECoG study.Research square · 2026Article
- A general model based on Riemannian manifold for stable decoding movement trajectory from ECoG signals.iScience · 2026Article
- Optimal sensor set for MEG-based spontaneous and intended speech decoding toward practical communication.Frontiers in human neuroscience · 2026Article
- 3M-CPSEED, An EEG-based Dataset for Chinese Pinyin Production in Overt, Mouthed, and Imagined Speech.Scientific data · 2025Article
- Real-time decoding of full-spectrum Chinese using brain-computer interface.Science advances · 2025Article
- Mimed speech as an intermediary state between overt and imagined speech production in an electrocorticography study.Scientific reports · 2025Article
- Progress, challenges and future of linguistic neural decoding with deep learning.Communications biology · 2025Review
- Natural sounds can be reconstructed from human neuroimaging data using deep neural network representation.PLoS biology · 2025Article
- VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language.Scientific data · 2025Article
- Transformer-based neural speech decoding from surface and depth electrode signals.Journal of neural engineering · 2025Article
- Supplementary motor area in speech initiation: A large-scale intracranial EEG evaluation of stereotyped word articulation.iScience · 2025Article
33 more citing papers are in PubMed but not listed here.
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance's identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate.
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