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A bilingual speech neuroprosthesis driven by cortical articulatory representations shared between languages


Advancements in decoding speech from brain activity have focused on decoding a single language. Hence, the extent to which bilingual speech production relies on unique or shared cortical activity across languages has remained unclear. Here, we leveraged electrocorticography, along with deep-learning and statistical natural-language models of English and Spanish, to record and decode activity from speech-motor cortex of a Spanish–English bilingual with vocal-tract and limb paralysis into sentences in either language. This was achieved without requiring the participant to manually specify the target language. Decoding models relied on shared vocal-tract articulatory representations across languages, which allowed us to build a syllable classifier that generalized across a shared set of English and Spanish syllables. Transfer learning expedited training of the bilingual decoder by enabling neural data recorded in one language to improve decoding in the other language. Overall, our findings suggest shared cortical articulatory representations that persist after paralysis and enable the decoding of multiple languages without the need to train separate language-specific decoders.

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Fig. 1: Implementation of a bilingual speech neuroprosthesis.
Fig. 2: Offline characterizations of the bilingual classification algorithms.
Fig. 3: A shared articulatory representation in the speech-motor cortex across languages.
Fig. 4: Rapid transfer learning between languages.

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Data availability

The data needed to recreate the main figures are provided as Source Data, and are also available in GitHub at The raw patient data are accessible to researchers from other institutions, but public sharing is restricted pursuant to our clinical trial protocol. Full access to the data will be granted on reasonable request to E.F.C. at, and a response can be expected in under 3 weeks. Shared data must be kept confidential and not provided to others unless approval is obtained. Shared data will not contain any information that may identify the participant, to protect their anonymity. Source data are provided with this paper.

Code availability

The code required to replicate the main findings of the study is available via GitHub at


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We thank our participant ‘Pancho’ for his tireless perseverance, commitment and dedication to the work described in this paper, and his family and caregivers for their incredible support. We also thank members of the Chang lab for feedback on the project; V. Her for administrative support; B. Spidel for imaging reconstruction; T. Dubnicoff for video editing; J. Davidson for help in designing initial bilingual stimuli; C. Kurtz-Miott, V. Anderson and S. Brosler for help with data collection with our participant; and the members of Karunesh Ganguly’s lab for help with the clinical trial. The National Institutes of Health (grant NIH U01 DC018671-01A1) and the William K. Bowes, Jr. Foundation supported authors S.L.M., J.R.L., D.A.M., M.E.D., M.P.S., K.T.L. and E.F.C. A.B.S. was supported by the National Institute of General Medical Sciences (NIGMS) Medical Scientist Training Program (Grant #T32GM007618) and by the National Institute On Deafness And Other Communication Disorders of the National Institutes of Health (award number F30DC021872). K.T.L. was supported by the National Science Foundation GRFP. A.T.-C. and K.G. did not have relevant funding for this work.

Author information

Authors and Affiliations



A.B.S. developed deep-learning classification and language models. J.R.L. developed speech detection models. D.A.M. implemented software for online decoding and data collection. A.B.S. generated figures and performed statistical analyses. A.B.S., along with J.R.L., wrote the manuscript with input from I.B.-G., S.L.M., K.T.L., D.A.M. and E.F.C. A.B.S. and D.A.M., along with J.R.L., S.L.M., I.B.-G. and M.E.D., designed the experiments, utterance sets and analyses. A.B.S., M.E.D. and M.P.S. led data collection with help from J.R.L., S.L.M., K.T.L. and D.A.M. M.P.S., A.T.-C., K.G. and E.F.C. performed regulatory and clinical supervision. E.F.C. conceived and supervised the study.

Corresponding author

Correspondence to Edward F. Chang.

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Competing interests

S.L.M., D.A.M., J.R.L. and E.F.C. are inventors on a pending provisional UCSF patent application relevant to the neural-decoding approaches used in this work (Application number: WO2022251472A1, 2022, WIPO PCT - International patent system). G.K.A. and E.F.C. are inventors on patent application PCT/US2020/028926; D.A.M. and E.F.C. are inventors on patent application PCT/US2020/043706; and E.F.C. is an inventor on patent US9905239B2. These patents are broadly relevant to the neural-decoding approaches used in this work. The remaining authors declare no competing interests.

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Nature Biomedical Engineering thanks Vikash Gilja, Jonas Obleser and Karim Oweiss for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Timing and information flow through the bilingual-sentence decoding system.

Shown is a more detailed schematic overview of the bilingual-sentence decoding system to complement Fig. 1a. Three levels of information are depicted: the neural features, the decoding system, and the output to the participant monitor. To start, the participant makes a speech attempt. This is detected by the system and cues activation of an ongoing decoding process. Following activation, a series of 3.5 s windows are cued to the participant. At the end of each window, after the full 3.5 s have passed, the neural features from that window are passed to the decoding process illustrated in Fig. 1a. Following a latency to conduct the decoding, the most likely beam from the process in Fig. 1a is displayed on the participant monitor. This process continues to occur for sequential 3.5 s windows until a window with no detected speech occurs. After such a window, the decoding is finalized and terminated. The system then listens for another speech attempt to activate and repeat the process.

Extended Data Fig. 2 Graphical depiction of bilingual-word classification.

Shown is a schematic of the bilingual-word classification process. Neural features (256 total; 128 HGA and 128 LFS time series over 3.5 s) are classified as a word in the bilingual vocabulary. Neural features are first processed by a temporal convolution. Next, the features are passed through three bidirectional GRU layers. The latent state from these layers is then read out by a dense, linear layer that emits probabilities over the 104 words in the bilingual vocabulary. This process is performed by 10 distinct models, each with a different weight initialization and trained on different folds of the data. The probabilities generated across these 10 models are averaged to create one probability vector across the bilingual vocabulary. This vector is finally split by language and the probability for a given word is broadcast to all conjugated forms of the word before being combined with the language model, as shown in Fig. 1a.

Extended Data Fig. 3 Neural-only chance sentence-decoding performance.

Shown are neural-only specific chance sentence-decoding distributions, alongside the neural-only decoding performance shown in Fig. 1. Here, we specifically computed a chance distribution with respect to neural-only decoding. We did this by shuffling the neural features and passing them through the classifier. The chance error rate was then computed the same way as for neural-only performance (**** P < 0.0001; two-sided Mann-Whitney U-test with 3-way Holm-Bonferroni correction for multiple comparisons). Distributions are over 21 online phrase-decoding blocks. Box plots in all panels depict median (horizontal line inside box), 25th and 75th percentiles (box), 25th and 75th percentiles +/- 1.5 times the interquartile range (whiskers), and outliers (diamonds).

Source data

Extended Data Fig. 4 Performance of attempted speech model on silent reading and listening.

For a subset of 10 bilingual words, we collected neural features during attempted speech, passive listening, and silent reading (roughly 250 trials in each paradigm). A model was trained on attempted speech data, using the same procedure throughout the manuscript, and evaluated on neural features from held-out attempted speech, passive listening, and silent reading trials. Performance was not significantly different from chance when evaluating the attempted speech model on listening or silent reading, in contrast to evaluation on attempted speech. This provides evidence that attempted speech neural features are specific to motor production of speech and not reflecting a process that strongly underlies listening or silent reading. Results are from 10-fold cross validation within each paradigm. Dashed line indicates chance performance (10%). Box plots in all panels depict median (horizontal line inside box), 25th and 75th percentiles (box), 25th and 75th percentiles +/- 1.5 times the interquartile range (whiskers), and outliers (diamonds).

Source data

Extended Data Fig. 5 Classification accuracy over the full 104 bilingual-words.

a, Shown is unmasked classification accuracy over the full 104 bilingual-words. The classifier retained stable performance without retraining (weights frozen at black dotted line) as in Fig. 2b. b, Classification performance before and after a 30-day break in recording without retraining (P = 0.31, two- sided Mann-Whitney U-test). Distributions are over 5 days. c, 10-fold cross validation (CV) accuracy over the unmasked 104 bilingual-words using all collected data. Median CV accuracy 47.24% (99% CI: [45.83,48.23] %). Distributions are over 10 non-overlapping folds. Box plots in all panels depict median (horizontal line inside box), 25th and 75th percentiles (box), 25th and 75th percentiles +/- 1.5 times the interquartile range (whiskers), and outliers (diamonds).

Source data

Extended Data Fig. 6 Acoustic similarity of words within the English and Spanish bilingual words.

For each word in the English vocabulary we calculated the mean pairwise mel-cepstral distortion (MCD) to all other English words. We repeated the same procedure for Spanish. Distributions are over 51 English and 50 Spanish words (shared words were excluded). English words have a significantly lower mean pairwise MCD (**** P < 0.0001, two-sided Mann-Whitney U-test). This indicates that English words, on average, are more acoustically confusable with other English words than Spanish words are with other Spanish words. Box plots in all panels depict median (horizontal line inside box), 25th and 75th percentiles (box), 25th and 75th percentiles +/- 1.5 times the interquartile range (whiskers), and outliers (diamonds).

Source data

Extended Data Fig. 7 Effects of re-training models daily during frozen-decoder evaluation.

Shown is a comparison between performance with and without re-calibration. (a) Shown is the performance without re-calibration for reference taken from (Fig. 2b). (b) Shown is the performance with re-training the classifier with sequential addition of each day’s data. (c) Shown are distributions of accuracy with and without re-training, demonstrating that small improvements may be found with re-training the decoders with each day’s data. Distributions are over 9 days in each boxplot (starting after the first-day when retraining is possible). Chance is 1.85% for English, 1.89% for Spanish, and 1.87% for all words (masked). Box plots in all panels depict median (horizontal line inside box), 25th and 75th percentiles (box), 25th and 75th percentiles +/- 1.5 times the interquartile range (whiskers), and outliers (diamonds).

Source data

Extended Data Fig. 8 Distinct contributions of HGA and LFS to classifier performance.

Shown are plots of electrode contributions for HGA against LFS, separately for English (left) and Spanish (right) trained models (as in Fig. 2d,e). The dotted lines indicate the 90th percentile of HGA and LFS contributions. The majority of electrodes only fall above the 90th percentile for one of HGA or LFS.

Source data

Extended Data Fig. 9 Full confusion matrix over all bilingual-words.

Full confusion matrix over the 104 bilingual-words. The sum of each row was normalized to 1, making confusion a proportion from (0-1). Predictions were generated using 10-fold cross validation over the full 104 bilingual-words with no masking (as in Extended Data Fig. 5).

Source data

Extended Data Fig. 10 Acoustic coverage of large-bilingual-phrase set.

We quantified the distribution of phonemes and phoneme place of articulation features to ensure the large-bilingual-phrase set covered a broad space in each language. We designed the large-bilingual-phrase set to sample a broad range of English (a) and Spanish (b) phonemes. We ensured that the relative proportion of phoneme place of articulation features was similar between English (c) and Spanish (d).

Source data

Supplementary information

Main Supplementary Information

Supplementary Notes, Methods, Figures, Tables, References and Video captions.

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Supplementary Video 1

A demonstration of online word-by-word bilingual sentence decoding from the brain of a participant with paralysis.

Supplementary Video 2

A demonstration of online word-by-word bilingual sentence decoding from the brain of a participant with paralysis, using three new sentences.

Supplementary Video 3

The participant using the bilingual speech neuroprosthesis has a conversation with a researcher.

Supplementary Data 1

Source data for Supplementary Fig. 1.

Supplementary Data 2

Source data for Supplementary Fig. 2.

Supplementary Data 3

Source data for Supplementary Fig. 3.

Supplementary Data 4

Source data for Supplementary Fig. 4.

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Silva, A.B., Liu, J.R., Metzger, S.L. et al. A bilingual speech neuroprosthesis driven by cortical articulatory representations shared between languages. Nat. Biomed. Eng (2024).

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