When designing neurotechnologies to assist people with communication disabilities, neuroscientists and engineers must consider both the speaker’s perspective and the listeners’ ability to judge the voluntariness and accuracy of decoded communication. This is particularly important in personally significant communication contexts for which there are profound legal and societal implications.
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Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain–computer interface. eLife 6, e18554 (2017).
Anumanchipalli, G. K., Chartier, J. & Chang, E. F. Speech synthesis from neural decoding of spoken sentences. Nature 568, 493–498 (2019).
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).
Herff, C. et al. Generating natural, intelligible speech from brain activity in motor, premotor, and inferior frontal cortices. Front. Neurosci. 13, 1267 (2019).
Moses, D. A. et al. Real-time decoding of question-and-answer speech dialogue using human cortical activity. Nat Commun 10, 3096 (2019).
Makin, J. G., Moses, D. A. & Chang, E. F. Machine translation of cortical activity to text with an encoder-decoder framework. Nat. Neurosci. 23, 575–582 (2020).
Dash, D., Ferrari, P. & Wang, J. Decoding imagined and spoken phrases from non-invasive neural (MEG) signals. Front. Neurosci. 14, 290 (2020).
Martin, S. et al. in Brain-Computer Interface Research: A State-of-the-Art Summary 7 (eds Guger, C., Mrachacz-Kersting, N. & Allison, B. Z.) 83–91 (Springer, 2019).
Moses, D. A. et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. New Engl. J. Med. 385, 217 (2021).
Kubler A., Nijboer, F. & Kleih S. in Brain Computer Interfaces Vol 168 (eds Ramsey, N. F. & del Millan, R.) 353–368 (Elsevier, 2020).
Han, C.-H., Müller, K.-R. & Hwang, H.-J. Brain-switches for asynchronous brain–computer interfaces: a systematic review. Electronics 9, 422 (2020).
Rainey, S. et al. Neuroprosthetic speech: the ethical significance of accuracy, control and pragmatics. Camb. Q. Healthc. Ethics 28, 657–670 (2019).
The authors gratefully acknowledge the comments of P. Wood on the use of communication neurotechnology in the context of living with late stage ALS. We also thank E. Snell (Snell Communications), T. Ladd (Cognixion) and the International Neuroethics Society for support for the workshop Breaking Through: Neurotechnology for High Consequence Communication and Decision-Making (Toronto, 2019) that inspired this Comment.
The authors declare no competing interests.
Brain/Neural Computer Interaction (BNCI) Horizon 2020 project: http://bnci-horizon-2020.eu/images/bncih2020/Appendix_C_End_Users.pdf
Institute of Electrical and Electronics Engineers (IEEE): https://standards.ieee.org/content/dam/ieee-standards/standards/web/documents/presentations/ieee-neurotech-for-bmi-standards-roadmap.pdf
International Neuroethics Society: www.neuroethicssociety.org
Organisation for Economic Cooperation and Development (OECD): https://www.oecd.org/science/recommendation-on-responsible-innovation-in-neurotechnology.htm
US Food and Drug Administration (FDA): https://www.fda.gov/regulatory-information/search-fda-guidance-documents/implanted-brain-computer-interface-bci-devices-patients-paralysis-or-amputation-non-clinical-testing
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Chandler, J.A., Van der Loos, K.I., Boehnke, S.E. et al. Building communication neurotechnology for high stakes communications. Nat Rev Neurosci 22, 587–588 (2021). https://doi.org/10.1038/s41583-021-00517-w