Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria
Open accessAbstract
BACKGROUND: Technology to restore the ability to communicate in paralyzed persons who cannot speak has the potential to improve autonomy and quality of life. An approach that decodes words and sentences directly from the cerebral cortical activity of such patients may represent an advancement over existing methods for assisted communication. METHODS: We implanted a subdural, high-density, multielectrode array over the area of the sensorimotor cortex that controls speech in a person with anarthria (the loss of the ability to articulate speech) and spastic quadriparesis caused by a brain-stem stroke. Over the course of 48 sessions, we recorded 22 hours of cortical activity while the participant attempted to say individual words from a vocabulary set of 50 words. We used deep-learning algorithms to create computational models for the detection and classification of words from patterns in the recorded cortical activity. We applied these computational models, as well as a natural-language model that yielded next-word probabilities given the preceding words in a sequence, to decode full sentences as the participant attempted to say them. RESULTS: We decoded sentences from the participant's cortical activity in real time at a median rate of 15.2 words per minute, with a median word error rate of 25.6%. In post hoc analyses, we detected 98% of the attempts by the participant to produce individual words, and we classified words with 47.1% accuracy using cortical signals that were stable throughout the 81-week study period. CONCLUSIONS: In a person with anarthria and spastic quadriparesis caused by a brain-stem stroke, words and sentences were decoded directly from cortical activity during attempted speech with the use of deep-learning models and a natural-language model. (Funded by Facebook and others; ClinicalTrials.gov number, NCT03698149.).
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Speech neuroprostheses could restore communication for people with paralysis, but naturalistic speed and expressivity have not yet been achieved.
“Speech neuroprostheses have the potential to restore communication to people living with paralysis, but naturalistic speed and expressivity are elusive1.”
From A high-performance neuroprosthesis for speech decoding and avatar control (2023) · cites this paper as [1]
The Background says decoding words and sentences from cortical activity could restore communication for paralyzed people who cannot speak. The Results report a median decoding rate of 15.2 words per minute, and the Methods describe a restricted 50-word set, supporting the point that this system had not achieved naturalistic communication. The paper does not assess speech expressivity or establish that expressivity remains elusive across speech neuroprostheses. (LLM, conf=0.83)
Experimental — accuracy not yet measuredDuring 14 days of data collection shortly after implantation, text decoding performance exceeded previous BCIs' communication speeds by a factor of 4 or more.
“During 14 days of data collection shortly after device implantation, we achieved high-performance text decoding, exceeding communication speeds of previous BCIs by a factor of 4 or more1,3,9”
From A high-performance neuroprosthesis for speech decoding and avatar control (2023) · cites this paper as [1]
The Results (Sentence Decoding; page unavailable) report real-time decoding at a median 15.2 words per minute, supporting the text-decoding portion of the claim. The Methods (Word and Sentence Task Design; page unavailable) describe 50 sessions over 81 weeks, not 14 days shortly after implantation. Our copy does not report a fourfold speed advantage over previous BCIs. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe vocabulary size was expanded by a factor of 20 relative to the previous direct-speech BCI.
“expanding the vocabulary size of our previous direct-speech BCI by a factor of 20 (ref. 1).”
From A high-performance neuroprosthesis for speech decoding and avatar control (2023) · cites this paper as [1]
The Methods (Word and Sentence Task Design; page unavailable) identify the cited direct-speech BCI as using a 50-word set. The paper does not report a subsequent vocabulary expansion or establish the claimed factor of 20. (LLM, conf=0.84)
Experimental — accuracy not yet measuredDemonstrations in able speakers with epilepsy served as proof-of-principle for future applications in individuals with vocal-tract paralysis that led to the first clinical trial for a speech neuroprosthesis using a chronically implanted 128-electrode ECoG array.
“Importantly, those demonstrations in able speakers with epilepsy served as a proof-of-principle for future applications in individuals with vocal-tract paralysis that led to the first clinical trial for a speech neuroprosthesis using a chronically implanted 128-electrode ECoG array12.”
From The speech neuroprosthesis (2024) · cites this paper as [12]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredAs a first approach to counter a lack of alignment, Moses et al. instructed a participant with anarthria to attempt to speak sentences word-by-word.
“As a first approach to counter a lack of alignment between neural activity and ground-truth targets, Moses et al.12 instructed a participant with anarthria to attempt to speak sentences word-by-word (with pauses between words).”
From The speech neuroprosthesis (2024) · cites this paper as [12]
The Methods (Word and Sentence Task Design; page not supplied) say the participant with anarthria attempted the words of each sentence in order. The Discussion identifies a time-alignment problem, but the Methods do not say he was instructed to pause between words; they say he attempted them at the fastest speed he could perform comfortably. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Speech-detection algorithms are highly successful in correctly identifying volitional attempts of users to overtly and silently speak.
“an additional safeguard is to use a speech-detection algorithm to gate any decoding process, which is highly successful in correctly identifying volitional attempts of users to overtly and silently speak12,120.”
From The speech neuroprosthesis (2024) · cites this paper as [12]
The Results, ‘Word Detection and Classification’ (page not provided), report that the detector identified 98% of one participant’s attempted word productions, supporting high detection of attempted speech. The study involved a person with anarthria; it does not establish comparable performance for both overt and silent speech across users. It also reports 968 spurious detections, so the result does not establish that detected events consistently represent volitional attempts. (LLM, conf=0.86)
Experimental — accuracy not yet measuredEarly speech BCI demonstrations have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary.
“Early demonstrations, although promising, have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary1–7.”
From A high-performance speech neuroprosthesis (2023) · cites this paper as [2]
The Methods (Word and Sentence Task Design; page unavailable) describe decoding 50 preset sentences using a 50-word vocabulary, and the Results (Sentence Decoding; page unavailable) report a median word error rate of 25.6% with language modeling. This supports the narrower point that this demonstration did not establish communication using unconstrained sentences and a large vocabulary. The paper does not establish that insufficient accuracy is the reason such communication had not been achieved across early demonstrations. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe 50-word vocabulary word error rate represents 2.7 times fewer errors than the previous state-of-the-art speech BCI.
“2.7 times fewer errors than the previous state-of-the-art speech BCI2”
From A high-performance speech neuroprosthesis (2023) · cites this paper as [2]
Results > Sentence Decoding (page not provided) reports a 25.6% median word error rate with language modeling for sentences drawn from a 50-word set. Our copy does not give the previous speech BCI’s error rate or establish the claimed 2.7-fold comparison. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPrior work has decoded speech from the brain activity of a person with paralysis, but only as text and with limited speed and vocabulary.
“Previous demonstrations have shown that it is possible to decode speech from the brain activity of a person with paralysis, but only in the form of text and with limited speed and vocabulary1,3.”
From A high-performance neuroprosthesis for speech decoding and avatar control (2023) · cites this paper as [1]
The median real-time text-decoding rate exceeded the participant's typical assistive-device communication rate of 14.2 words per minute and was closer to naturalistic speaking rates than previously reported communication neuroprostheses.
“This decoding rate exceeds our participant’s typical communication rate using her assistive device (14.2 WPM; Supplementary Note 2) and is closer to naturalistic speaking rates than has been previously reported with communication neuroprostheses1,3,7–9.”
From A high-performance neuroprosthesis for speech decoding and avatar control (2023) · cites this paper as [1]
Functional speech-decoding performance can be achieved after a relatively short period of data collection compared with previous work and is likely to improve with more data.
“These results show that functional speech-decoding performance can be achieved after a relatively short period of data collection compared to that of our previous work1,3 and is likely to continue to improve with more data.”
From A high-performance neuroprosthesis for speech decoding and avatar control (2023) · cites this paper as [1]
Initial attempts to decode text from brain activity in a person with anarthria during attempted speech had limitations such as slow decoding rates and small vocabulary sizes.
“Initial efforts to decode text from the brain activity ofa person with anarthria during attempted speech had various limitations, including slow decoding rates and small vocabulary sizes1,3.”
From A high-performance neuroprosthesis for speech decoding and avatar control (2023) · cites this paper as [1]
Studies on cortical encoding of speech features laid groundwork for a speech neuroprosthesis that decoded text from cortical activity of an individual with incomplete locked-in syndrome who relied on AAC.
“Studies furthering our basic understanding of the cortical encoding of speech features, notably motor control of the vocal-tract articulators during speech, laid the groundwork for a speech neuroprosthesis that decoded text in the form of words and sentences from the cortical activity of an individual with incomplete locked-in syndrome who relied on AAC methods to communicate12.”
From The speech neuroprosthesis (2024) · cites this paper as [12]
Durable stability has enabled ECoG-based neuroprostheses without the need for daily calibration by the user.
“Despite this, durable stability has enabled ECoG-based neuroprostheses without the need for daily calibration by the user12,14,94,95.”
From The speech neuroprosthesis (2024) · cites this paper as [12]
A language model combined neural-based probabilities of each word with linguistic likelihoods of word sequences, improving WERs of the decoded sentences.
“A language model combined the neural-based probabilities of each word with linguistic likelihoods of word sequences, improving WERs of the decoded sentences12 (Fig. 1; Moses et al. 2021; Supplementary Table 1).”
From The speech neuroprosthesis (2024) · cites this paper as [12]
Most early studies in individuals with vocal-tract paralysis involve only single participants.
“Given that most early studies in individuals with vocal-tract paralysis involve only single participants12–16,64,95,120, great care is needed when comparing results, as the broad spectrum of disease presentations is not sampled”
From The speech neuroprosthesis (2024) · cites this paper as [12]
Several groups have performed real-time brain-computer interface studies to restore lost speech using implanted electrocorticography (ECoG) or intracortical multielectrode arrays.
“Several groups have performed real-time braincomputer interface studies to restore lost speech using implanted electrocorticography (ECoG) 17-20 , including a report published in the Journal 17 , or intracortical multielectrode arrays 21 .”
From An Accurate and Rapidly Calibrating Speech Neuroprosthesis (2024) · cites this paper as [17]
The left precentral gyrus is an important cortical region for coordinating motor activities related to speech.
“We implanted four microelectrode arrays (NeuroPort Array, Blackrock Neurotech, Salt Lake City, Utah, USA) into the left precentral gyrus, an important cortical region for coordinating motor activities related to speech 17,19,21 .”
From An Accurate and Rapidly Calibrating Speech Neuroprosthesis (2024) · cites this paper as [17]
Recent studies have shown that high-density electrocorticography can decode speech representations, allowing words and short sentences to be articulated.
“More recent work has demonstrated the utility of high-density ECoG in decoding neural representations of speech, enabling the articulation of words and short sentences 6,69,99 .”
From The Layer 7 Cortical Interface: A Scalable and Minimally Invasive Brain–Computer Interface Platform (2022) · cites this paper as [6]
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Inner speech in motor cortex and implications for speech neuroprostheses
Kunz, E. M., Abramovich Krasa, B., Kamdar, F., Avansino, D. T., Hahn, N., Yoon, S., Singh, A., Nason-Tomaszewski, S. R., Card, N. S., Jude, J. J., et al. (2025) · Cell · doi:10.1016/j.cell.2025.06.015 · Cited by 55 · see citing works
“For the 50-word vocabulary sessions this evaluation set consisted of the 50 sentences from Moses et al., 14 also used for evaluation in Willett et al. and Card et al. 12,17 For the 125,000-word vocabulary evaluation block, we used a random selection of sentences from the Switchboard corpus similar…”
InfluentialDirect speech reconstruction from sensorimotor brain activity with optimized deep learning models
Berezutskaya, J., Freudenburg, Z. V., Vansteensel, M. J., Aarnoutse, E. J., Ramsey, N. F., & van Gerven, M. A. J. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ace8be · Cited by 42 · see citing works
Pennartz, C. M. A. (2022) · Behavioural Brain Research · doi:10.1016/j.bbr.2022.113969 · Cited by 40 · see citing works
BRAND: a platform for closed-loop experiments with deep network models
Ali, Y. H., Bodkin, K., Rigotti-Thompson, M., Patel, K., Card, N. S., Bhaduri, B., Nason-Tomaszewski, S. R., Mifsud, D. M., Hou, X., Nicolas, C., et al. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad3b3a · Cited by 32 · see citing works
Neurorights, mental privacy, and mind reading
Brown, C. M. L. (2024) · Neuroethics · doi:10.1007/s12152-024-09568-z · Cited by 33 · see citing works
The Layer 7 Cortical Interface: A Scalable and Minimally Invasive Brain–Computer Interface Platform
Hettick, M., Ho, E., Poole, A. J., Monge, M., Papageorgiou, D., Takahashi, K., LaMarca, M., Trietsch, D., Reed, K., Murphy, M., et al. (2022) · doi:10.1101/2022.01.02.474656 · Cited by 31 · see citing works
Neural control of lexical tone production in human laryngeal motor cortex
Lu, J., Li, Y., Zhao, Z., Liu, Y., Zhu, Y., Mao, Y., Wu, J., & Chang, E. F. (2023) · Nature Communications · doi:10.1038/s41467-023-42175-9 · Cited by 26 · 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
Thomas, T. M., Singh, A., Bullock, L. P., Liang, D., Morse, C. W., Scherschligt, X., Seymour, J. P., & Tandon, N. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ace9fb · Cited by 18 · see citing works
An accurate and rapidly calibrating speech neuroprosthesis
Card, N. S., Wairagkar, M., Iacobacci, C., Hou, X., Singer-Clark, T., Willett, F. R., Kunz, E. M., Fan, C., Vahdati Nia, M., Deo, D. R., et al. (2023) · doi:10.1101/2023.12.26.23300110 · Cited by 244 · see citing works
“We used two metrics to analyze the speech decoding performance: phoneme error rate (PER) and word error rate (WER), consistent with previous speech decoding studies 16,18,20 .”
BackgroundMethodInfluentialOnline internal speech decoding from single neurons in a human participant
Wandelt, S. K., Bjånes, D. A., Pejsa, K., Lee, B., Liu, C., & Andersen, R. A. (2022) · doi:10.1101/2022.11.02.22281775 · Cited by 17 · see citing works
The ethical significance of user-control in AI-driven speech-BCIs: a narrative review
van Stuijvenberg, O. C., Samlal, D. P. S., Vansteensel, M. J., Broekman, M. L. D., & Jongsma, K. R. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1420334 · Cited by 13 · see citing works
Direct Speech Reconstruction from Sensorimotor Brain Activity with Optimized Deep Learning Models
Berezutskaya, J., Freudenburg, Z. V., Vansteensel, M. J., Aarnoutse, E. J., Ramsey, N. F., & van Gerven, M. A. J. (2022) · doi:10.1101/2022.08.02.502503 · Cited by 42 · see citing works
- Authors
- Moses, D. A., Metzger, S. L., Liu, J. R., Anumanchipalli, G. K., Makin, J. G., Sun, P. F., Chartier, J., Dougherty, M. E., Liu, P. M., Abrams, G. M., Tu-Chan, A., Ganguly, K., & Chang, E. F.
- Affiliations
- University of California, San Francisco (US)
- ORCID
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2021-07-15
- Volume
- 385
- Issue
- 3
- Pages
- 217-227
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC8972947
- OpenAlex
- W3180220247
- Semantic Scholar
- 9a9cbb53ac1ebfedee423f0ef9230b652e322dbc
- Cited by
- 582
- References
- 39
- Citations by year
- 2026: 105 · 2025: 127 · 2024: 123 · 2023: 114 · 2022: 77 · 2021: 34 · 2019: 2
- FWCI
- 45.77
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Facebook (Sponsored Academic Research Agreement)
- National Institutes of Health (U01 NS098971-01)
- William K. Bowes, Jr. Foundation
- Howard Hughes Medical Institute
- Shurl and Kay Curci Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- neural prostheses, speech decoding, anarthria, paralysis, brain-computer interface, sensorimotor cortex, deep learning, natural language models, brainstem infarction, spastic quadriplegia, word error rate
- MeSH terms
- Electrocorticography, Deep Learning, Adult, Dysarthria, Electrodes, Implanted, Humans, Male, Natural Language Processing, Quadriplegia, Speech, Brain Stem Infarctions, Neural Prostheses, Brain-Computer Interfaces, Sensorimotor Cortex
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.
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