Inner speech in motor cortex and implications for speech neuroprostheses

Speech brain-computer interfaces (BCIs) show promise in restoring communication to people with paralysis but have also prompted discussions regarding their potential to decode private inner speech. Separately, inner speech may be a way to bypass the current approach of requiring speech BCI users to physically attempt speech, which is fatiguing and can slow communication. Using multi-unit recordings from four participants, we found that inner speech is robustly represented in the motor cortex and that imagined sentences can be decoded in real time. The representation of inner speech was highly correlated with attempted speech, though we also identified a neural "motor-intent" dimension that differentiates the two. We investigated the possibility of decoding private inner speech and found that some aspects of free-form inner speech could be decoded during sequence recall and counting tasks. Finally, we demonstrate high-fidelity strategies that prevent speech BCIs from unintentionally decoding private inner speech.

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Publication Details

Journal
Cell
Published
2025-08-01
DOI
https://doi.org/10.1016/j.cell.2025.06.015
Citations
17
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
24.33

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article

Inner speech in motor cortex and implications for speech neuroprostheses

Samuel R. Nason, Jaimie M. Henderson, Carrina Iacobacci, Brandon G. Jacques et al.
17 citations
Cell
EEG and Brain-Computer Interfaces
24.33
article

Inner speech in motor cortex and implications for speech neuroprostheses

Samuel R. Nason, Jaimie M. Henderson, Carrina Iacobacci, Brandon G. Jacques, Donald T. Avansino, Leigh R. Hochberg, Shaul Druckmann, David M. Brandman, Daniel B. Rubin, Foram Kamdar, Akansha Singh, Sergey D. Stavisky, Chethan Pandarinath, Francis R. Willett, Justin Jude, Ziv M. Williams, Erin M. Kunz, Nicholas S. Card, Nick Hahn, Payton Bechefsky, Soo Jin Yoon, Nicholas Au Yong, Benyamin Abramovich Krasa
article en
17 citations

Abstract

Speech brain-computer interfaces (BCIs) show promise in restoring communication to people with paralysis but have also prompted discussions regarding their potential to decode private inner speech. Separately, inner speech may be a way to bypass the current approach of requiring speech BCI users to physically attempt speech, which is fatiguing and can slow communication. Using multi-unit recordings from four participants, we found that inner speech is robustly represented in the motor cortex and that imagined sentences can be decoded in real time. The representation of inner speech was highly correlated with attempted speech, though we also identified a neural "motor-intent" dimension that differentiates the two. We investigated the possibility of decoding private inner speech and found that some aspects of free-form inner speech could be decoded during sequence recall and counting tasks. Finally, we demonstrate high-fidelity strategies that prevent speech BCIs from unintentionally decoding private inner speech.

CellVol. 188(17)
Georgia Institute of Technology (US), Howard Hughes Medical Institute (US), Harvard University (US), Emory University (US), Neurosciences Institute (US), The Wallace H. Coulter Department of Biomedical Engineering (US), Providence VA Medical Center (US), Massachusetts General Hospital (US), Center for Neuro-Oncology (US), Neurological Surgery (US), University of California, Davis (US), Stanford University (US)
Howard Hughes Medical Institute, Simons Foundation, A.P. Giannini Foundation, National Institute on Deafness and Other Communication Disorders, National Institute of Neurological Disorders and Stroke, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Peace, Justice and strong institutions
Openalex Percentile: Top 0%
EEG and Brain-Computer Interfaces
24.33
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