Herff, C. et al. Brain-to-text: decoding spoken phrases from phone representations in the brain. Front. Neurosci. 9, 217 (2015).
Google Scholar
Moses, D. A. et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N. Engl. J. Med. 385, 217–227 (2021).
Google Scholar
Anumanchipalli, G. K., Chartier, J. & Chang, E. F. Speech synthesis from neural decoding of spoken sentences. Nature 568, 493–498 (2019).
Google Scholar
Herff, C. et al. Generating natural, intelligible speech from brain activity in motor, premotor, and inferior frontal cortices. Front. Neurosci. 13, 1267 (2019).
Google Scholar
Kellis, S. et al. Decoding spoken words using local field potentials recorded from the cortical surface. J. Neural Eng. 7, 056007 (2010).
Google Scholar
Pei, X., Barbour, D. L., Leuthardt, E. C. & Schalk, G. Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans. J. Neural Eng. 8, 046028 (2011).
Google Scholar
Mugler, E. M. et al. Direct classification of all American English phonemes using signals from functional speech motor cortex. J. Neural Eng. 11, 035015 (2014).
Google Scholar
Willett, F. R., Avansino, D. T., Hochberg, L. R., Henderson, J. M. & Shenoy, K. V. High-performance brain-to-text communication via handwriting. Nature 593, 249–254 (2021).
Google Scholar
Yuan, J., Liberman, M. & Cieri, C. Towards an integrated understanding of speaking rate in conversation. In 9th Intl Conf. on Spoken Language Processing https://doi.org/10.21437/Interspeech.2006-204 (2006).
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
Google Scholar
Broca, P. Nouvelle observation d’aphemie produite par une lesion de la troisieme circonvolution frontale. Bull. Soc. Anat. 2, 398–407 (1861).
Friederici, A. D. & Gierhan, S. M. The language network. Curr. Opin. Neurobiol. 23, 250–254 (2013).
Google Scholar
Ardila, A., Bernal, B. & Rosselli, M. How localized are language brain areas? A review of Brodmann areas involvement in oral language. Arch. Clin. Neuropsychol. 31, 112–122 (2016).
Google Scholar
Long, M. A. et al. Functional segregation of cortical regions underlying speech timing and articulation. Neuron 89, 1187–1193 (2016).
Google Scholar
Tate, M. C., Herbet, G., Moritz-Gasser, S., Tate, J. E. & Duffau, H. Probabilistic map of critical functional regions of the human cerebral cortex: Broca’s area revisited. Brain 137, 2773–2782 (2014).
Google Scholar
Flinker, A. et al. Redefining the role of Broca’s area in speech. Proc. Natl Acad. Sci. USA 112, 2871–2875 (2015).
Google Scholar
Gajardo-Vidal, A. et al. Damage to Broca’s area does not contribute to long-term speech production outcome after stroke. Brain 144, 817–832 (2021).
Google Scholar
Andrews, J. P. et al. Dissociation of Broca’s area from Broca’s aphasia in patients undergoing neurosurgical resections. J. Neurosurg. 138, 847–857 (2022).
Google Scholar
Bouchard, K. E., Mesgarani, N., Johnson, K. & Chang, E. F. Functional organization of human sensorimotor cortex for speech articulation. Nature 495, 327–332 (2013).
Google Scholar
Godfrey, J. J., Holliman, E. C. & McDaniel, J. SWITCHBOARD: telephone speech corpus for research and development. In IEEE Intl Conf. on Acoustics, Speech, and Signal Processing https://doi.org/10.1109/ICASSP.1992.225858 (IEEE, 1992).
Hinton, G. et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29, 82–97 (2012).
Google Scholar
Graves, A., Mohamed, A. & Hinton, G. Speech recognition with deep recurrent neural networks. In 2013 IEEE Intl Conf. on Acoustics, Speech and Signal Processing https://doi.org/10.1109/ICASSP.2013.6638947 (IEEE, 2013).
Xiong, W. et al. The Microsoft 2017 Conversational Speech Recognition System. In 2018 IEEE Intl Conf. on Acoustics, Speech and Signal Processing (ICASSP) https://doi.org/10.1109/ICASSP.2018.8461870 (IEEE, 2018).
Dyer, E. L. et al. A cryptography-based approach for movement decoding. Nat. Biomed. Eng. 1, 967–976 (2017).
Google Scholar
Farshchian, A. et al. Adversarial domain adaptation for stable brain-machine interfaces. Preprint at https://doi.org/10.48550/arXiv.1810.00045 (2019).
Degenhart, A. D. et al. Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-020-0542-9 (2020).
Karpowicz, B. M. et al. Stabilizing brain-computer interfaces through alignment of latent dynamics. Preprint at bioRxiv https://doi.org/10.1101/2022.04.06.487388 (2022).
Pels, E. G. M., Aarnoutse, E. J., Ramsey, N. F. & Vansteensel, M. J. Estimated prevalence of the target population for brain-computer interface neurotechnology in the Netherlands. Neurorehabil. Neural Repair 31, 677–685 (2017).
Google Scholar
Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain-computer interface. eLife 6, e18554 (2017).
Google Scholar
Räihä, K.-J. & Ovaska, S. An exploratory study of eye typing fundamentals: dwell time, text entry rate, errors, and workload. In Proc. SIGCHI Conf. on Human Factors in Computing Systems https://doi.org/10.1145/2207676.2208711 (Association for Computing Machinery, 2012).
Sussillo, D., Stavisky, S. D., Kao, J. C., Ryu, S. I. & Shenoy, K. V. Making brain–machine interfaces robust to future neural variability. Nat. Commun. 7, 13749 (2016).
Google Scholar
Nurmikko, A. Challenges for large-scale cortical interfaces. Neuron 108, 259–269 (2020).
Google Scholar
Vázquez-Guardado, A., Yang, Y., Bandodkar, A. J. & Rogers, J. A. Recent advances in neurotechnologies with broad potential for neuroscience research. Nat. Neurosci. 23, 1522–1536 (2020).
Google Scholar
Rubin, D. B. et al. Interim safety profile from the feasibility study of the BrainGate neural interface system. Neurology 100, e1177–e1192 (2023).
Google Scholar
Musk, E. & Neuralink An integrated brain-machine interface platform with thousands of channels. J. Med. Internet Res. 21, e16194 (2019).
Google Scholar
Sahasrabuddhe, K. et al. The Argo: a high channel count recording system for neural recording in vivo. J. Neural Eng. https://doi.org/10.1088/1741-2552/abd0ce (2020).
Google Scholar
He, Y. et al. Streaming end-to-end speech recognition for mobile devices. In ICASSP 2019 – 2019 IEEE Intl Conf. on Acoustics, Speech and Signal Processing (ICASSP) https://doi.org/10.1109/ICASSP.2019.8682336 (IEEE, 2019).
Aiello, A. A Phonetic Examination of California (UCSC Linguistics Research Center, 2010).