ReviewNeurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics2019
The Potential for a Speech Brain-Computer Interface Using Chronic Electrocorticography.
Review in Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed, 112 citations in OpenAlex.
- 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
- A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language.Scientific data · 2026Article
- Decoding intended speech with an intracortical brain-computer interface in a person with long-standing anarthria and locked-in syndrome.Cell reports · 2026Article
- 3M-CPSEED, An EEG-based Dataset for Chinese Pinyin Production in Overt, Mouthed, and Imagined Speech.Scientific data · 2025Article
- Review
- VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language.Scientific data · 2025Article
- Whole-brain dynamics of articulatory, acoustic and semantic speech representations.Communications biology · 2025Article
- Signal acquisition of brain-computer interfaces: A medical-engineering crossover perspective review.Fundamental research · 2025Review
- A Comprehensive Survey of Brain-Computer Interface Technology in Health care: Research Perspectives.Journal of medical signals and sensors · 2025Review
- Improved evaluation of waveform reconstruction in speech decoding based on invasive brain-computer interfaces.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Real-time detection of spoken speech from unlabeled ECoG signals: A pilot study with an ALS participant.medRxiv : the preprint server for health sciences · 2024Article
- Iterative alignment discovery of speech-associated neural activity.Journal of neural engineering · 2024Article
- Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment.Cureus · 2024Review
- Representation of internal speech by single neurons in human supramarginal gyrus.Nature human behaviour · 2024Article
- Feasibility of decoding covert speech in ECoG with a Transformer trained on overt speech.Scientific reports · 2024Article
- Adaptive LDA Classifier Enhances Real-Time Control of an EEG Brain-Computer Interface for Decoding Imagined Syllables.Brain sciences · 2024Article
- A neural speech decoding framework leveraging deep learning and speech synthesis.Nature machine intelligence · 2024Article
- Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome.Journal of neuroengineering and rehabilitation · 2023Review
- Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.Journal of neural engineering · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
A brain-computer interface (BCI) is a technology that uses neural features to restore or augment the capabilities of its user. A BCI for speech would enable communication in real time via neural correlates of attempted or imagined speech. Such a technology would potentially restore communication and improve quality of life for locked-in patients and other patients with severe communication disorders. There have been many recent developments in neural decoders, neural feature extraction, and brain recording modalities facilitating BCI for the control of prosthetics and in automatic speech recognition (ASR). Indeed, ASR and related fields have developed significantly over the past years, and many lend many insights into the requirements, goals, and strategies for speech BCI. Neural speech decoding is a comparatively new field but has shown much promise with recent studies demonstrating semantic, auditory, and articulatory decoding using electrocorticography (ECoG) and other neural recording modalities. Because the neural representations for speech and language are widely distributed over cortical regions spanning the frontal, parietal, and temporal lobes, the mesoscopic scale of population activity captured by ECoG surface electrode arrays may have distinct advantages for speech BCI, in contrast to the advantages of microelectrode arrays for upper-limb BCI. Nevertheless, there remain many challenges for the translation of speech BCIs to clinical populations. This review discusses and outlines the current state-of-the-art for speech BCI and explores what a speech BCI using chronic ECoG might entail.
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What OpenQuestion holds
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.