ArticleJournal of neural engineering2018
Real-time classification of auditory sentences using evoked cortical activity in humans.
Article in Journal of neural engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Perspectives in conducting task-based research in pediatric surgical epilepsy patients.bioRxiv : the preprint server for biology · 2026Article
- Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.Exploration (Beijing, China) · 2026Review
- Implications of shared motor and perceptual activations on the sensorimotor cortex for neuroprosthetic decoding.Journal of neural engineering · 2025Article
- Improved evaluation of waveform reconstruction in speech decoding based on invasive brain-computer interfaces.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A bilingual speech neuroprosthesis driven by cortical articulatory representations shared between languages.Nature biomedical engineering · 2024Article
- Article
- Generalizable spelling using a speech neuroprosthesis in an individual with severe limb and vocal paralysis.Nature communications · 2022Article
- Dataset of Speech Production in intracranial.Electroencephalography.Scientific data · 2022Article
- On the similarities of representations in artificial and brain neural networks for speech recognition.Frontiers in computational neuroscience · 2022Article
- Brain-Computer Interface: Applications to Speech Decoding and Synthesis to Augment Communication.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022Review
- Real-time synthesis of imagined speech processes from minimally invasive recordings of neural activity.Communications biology · 2021Article
- Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria.The New England journal of medicine · 2021Article
- Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG.Sensors (Basel, Switzerland) · 2020Article
- Using Coherence-based spectro-spatial filters for stimulus features prediction from electro-corticographic recordings.Scientific reports · 2020Article
- Real-time decoding of question-and-answer speech dialogue using human cortical activity.Nature communications · 2019Article
- Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2019Review
- Machine Learning Approaches to Analyze Speech-Evoked Neurophysiological Responses.Journal of speech, language, and hearing research : JSLHR · 2019Review
- Generating Natural, Intelligible Speech From Brain Activity in Motor, Premotor, and Inferior Frontal Cortices.Frontiers in neuroscience · 2019Article
- Decoding speech from spike-based neural population recordings in secondary auditory cortex of non-human primates.Communications biology · 2019Article
- The Potential for a Speech Brain-Computer Interface Using Chronic Electrocorticography.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2019Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectiveRecent research has characterized the anatomical and functional basis of speech perception in the human auditory cortex. These advances have made it possible to decode speech information from activity in brain regions like the superior temporal gyrus, but no published work has demonstrated this ability in real-time, which is necessary for neuroprosthetic brain-computer interfaces. APPROACH: Here, we introduce a real-time neural speech recognition (rtNSR) software package, which was used to classify spoken input from high-resolution electrocorticography signals in real-time. We tested the system with two human subjects implanted with electrode arrays over the lateral brain surface. Subjects listened to multiple repetitions of ten sentences, and rtNSR classified what was heard in real-time from neural activity patterns using direct sentence-level and HMM-based phoneme-level classification schemes. MAIN
resultsWe observed single-trial sentence classification accuracies of [Formula: see text] or higher for each subject with less than 7 minutes of training data, demonstrating the ability of rtNSR to use cortical recordings to perform accurate real-time speech decoding in a limited vocabulary setting. SIGNIFICANCE: Further development and testing of the package with different speech paradigms could influence the design of future speech neuroprosthetic applications.
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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.