Toward a Speech Neuroprosthesis
Open accessAbstract
Spoken communication is a basic human function.As such, loss of the ability to speak can be devastating for affected individuals.Stroke or neurodegenerative conditions, such as amyotrophic lateral sclerosis, can result in paralysis or dysfunction of vocal structures that produce speech.Current options are assistive devices that use residual movements, for example, cheek twitches or eye movements, to navigate alphabet displays to type out words. 1 While some users depend on these alternative communication approaches, these devices tend to be slow, error-prone, and laborious.A next generation of rehabilitative technologies currently being developed, called brain-computer interfaces (BCIs), directly read out brain signals to replace lost function.The application of neuroprostheses to restore speech has the potential to improve the quality of life of patients with neurological disease, but also including patients who have lost speech from vocal tract injury (eg, from cancer or cancer-related surgery).
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Communication rates for neuroprostheses controlled by attempted hand movements remain slower and less expressive than natural speech.
“However, communication rates for neuroprostheses controlled by attempted hand movements remain slower and less expressive than natural speech10.”
From The speech neuroprosthesis (2024) · cites this paper as [10]
In “Toward a Speech Neuroprosthesis” (page unavailable), the paper says neuroprostheses that decode arm-movement signals to control a spelling cursor achieve under 10 words per minute, versus 120–150 words per minute for natural speech. It describes speech features such as prosodic intonation, but does not directly establish that hand-movement-controlled neuroprostheses are less expressive. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSpeech enables efficient and rapid communication at rates of around 150-200 words per minute in conversational settings.
“Speech enables efficient and rapid communication at rates of around 150–200 words per minute (WPM) in conversational settings10.”
From The speech neuroprosthesis (2024) · cites this paper as [10]
The 'Toward a Speech Neuroprosthesis' section describes speech as efficient, rapid communication and gives natural speaking rates of 120–150 words per minute in healthy speakers. It does not support the cited range of 150–200 words per minute or specify that rate for conversational settings. No page number is supplied in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAble speakers often speak at rates around 150 WPM during conversational speech.
“Able speakers often speak at rates of around 150 WPM during conversational speech10; however, for individuals with vocal-tract paralysis, their maximal rate of attempted speech can vary considerably and may depend on their residual articulatory capacities and degree of comfort4,133,134.”
From The speech neuroprosthesis (2024) · cites this paper as [10]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Speech synthesis from neural decoding of spoken sentences
Anumanchipalli, G. K., Chartier, J., & Chang, E. F. (2019) · Nature · doi:10.1038/s41586-019-1119-1 · Cited by 883 · see citing works
Self-citationDeep Learning—A Technology With the Potential to Transform Health Care
Hinton, G. (2018) · JAMA · doi:10.1001/jama.2018.11100 · Cited by 667 · see citing works
Pandarinath, C., Nuyujukian, P., Blabe, C. H., Sorice, B. L., Saab, J., Willett, F. R., Hochberg, L. R., Shenoy, K. V., & Henderson, J. M. (2017) · eLife · doi:10.7554/elife.18554 · Cited by 566 · see citing works
Real-time decoding of question-and-answer speech dialogue using human cortical activity
Moses, D. A., Leonard, M. K., Makin, J. G., & Chang, E. F. (2019) · Nature Communications · doi:10.1038/s41467-019-10994-4 · Cited by 224 · see citing works
Self-citationEncoding of Articulatory Kinematic Trajectories in Human Speech Sensorimotor Cortex
Chartier, J., Anumanchipalli, G. K., Johnson, K., & Chang, E. F. (2018) · Neuron · doi:10.1016/j.neuron.2018.04.031 · Cited by 208 · see citing works
Self-citationNoble gases confirm plume-related mantle degassing beneath Southern Africa
Gilfillan, S., Györe, D., Flude, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12944-6 · Cited by 177 · see citing works
Koch Fager, S., Fried-Oken, M., Jakobs, T., & Beukelman, D. R. (2019) · Augmentative and Alternative Communication · doi:10.1080/07434618.2018.1556730 · Cited by 107 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [3]Mugler EM. Investigation of Speech for Communicative BrainComputer Interface [doctoral dissertation]. Chicago, IL: University of Illinois at Chicago; 2014.
Cited by
Sorted by citations
Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus
Wilson, G. H., Stavisky, S. D., Willett, F. R., Avansino, D. T., Kelemen, J. N., Hochberg, L. R., Henderson, J. M., Druckmann, S., & Shenoy, K. V. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abbfef · Cited by 103 · see citing works
Brain-Computer Interface: Applications to Speech Decoding and Synthesis to Augment Communication
Luo, S., Rabbani, Q., & Crone, N. E. (2022) · Neurotherapeutics · doi:10.1007/s13311-022-01190-2 · Cited by 106 · see citing works
Silva, A. B., Littlejohn, K. T., Liu, J. R., Moses, D. A., & Chang, E. F. (2024) · Nature Reviews Neuroscience · doi:10.1038/s41583-024-00819-9 · Cited by 103 · see citing works
“Able speakers often speak at rates of around 150 WPM during conversational speech 10 ; however, for individuals with vocal-tract paralysis, their maximal rate of attempted speech can vary considerably and may depend on their residual articulatory capacities and degree of comfort 4,133,134 .”
Self-citationBrain2Char: a deep architecture for decoding text from brain recordings
Sun, P., Anumanchipalli, G. K., & Chang, E. F. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc742 · Cited by 77 · see citing works
Self-citationAngrick, M., Luo, S., Rabbani, Q., Candrea, D. N., Shah, S., Milsap, G. W., Anderson, W. S., Gordon, C. R., Rosenblatt, K. R., Clawson, L., et al. (2023) · doi:10.1101/2023.06.30.23291352 · Cited by 95 · see citing works
- Authors
- Chang, E. F., & Anumanchipalli, G. K.
- Affiliations
- University of California, San Francisco (US)
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2020-02-04
- Volume
- 323
- Issue
- 5
- Pages
- 413
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC10560394
- OpenAlex
- W2997091249
- Cited by
- 35
- References
- 7
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 5 · 2023: 5 · 2022: 6 · 2021: 4 · 2020: 5
- FWCI
- 2.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (U01NS098971)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Keywords
- brain-computer interface, speech recovery, amyotrophic lateral sclerosis, stroke, paralysis, assistive communication devices, neurorehabilitation
- MeSH terms
- Humans, Speech, Speech Disorders, Neural Prostheses
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
