Decoding intended speech with an intracortical brain-computer interface in a person with long-standing anarthria and locked-in syndrome

Intracortical brain-computer interfaces (iBCIs) for decoding intended speech have provided individuals with ALS and severe dysarthria an intuitive method for high-throughput communication. These advances have been demonstrated in individuals who are still able to vocalize and move speech articulators. Here, we decoded intended speech from an individual with long-standing anarthria, locked-in syndrome, and ventilator dependence due to advanced symptoms of ALS. We found that phonemes, words, and higher order language units could be decoded well above chance. While sentence decoding accuracy was below that of demonstrations in participants with dysarthria, we attained an extensive characterization of neural signals underlying speech in a person with locked-in syndrome and identify directions for future improvement. These include closed-loop speech imagery training and decoding linguistic (rather than phonemic) units from neural signals in middle precentral gyrus to augment decoding at the sentence level. These results demonstrate that usable speech decoding from motor cortex may be feasible in people with anarthria and ventilator dependence.

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Publication Details

Journal
Cell Reports
Published
2026-03-31
DOI
https://doi.org/10.1016/j.celrep.2026.117162
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00

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article

Decoding intended speech with an intracortical brain-computer interface in a person with long-standing anarthria and locked-in syndrome

John D. Simeral, Maitreyee Wairagkar, Leigh R. Hochberg, David M. Brandman et al.
Cell Reports
EEG and Brain-Computer Interfaces
article

Decoding intended speech with an intracortical brain-computer interface in a person with long-standing anarthria and locked-in syndrome

John D. Simeral, Maitreyee Wairagkar, Leigh R. Hochberg, David M. Brandman, Daniel B. Rubin, Stephanie Haro, Sergey D. Stavisky, Hadar Levi-Aharoni, Hiroaki Hashimoto, Justin Jude, Nicholas S. Card, Alexander J. Acosta, Ziv M. Williams, Sydney S. Cash
article en

Abstract

Intracortical brain-computer interfaces (iBCIs) for decoding intended speech have provided individuals with ALS and severe dysarthria an intuitive method for high-throughput communication. These advances have been demonstrated in individuals who are still able to vocalize and move speech articulators. Here, we decoded intended speech from an individual with long-standing anarthria, locked-in syndrome, and ventilator dependence due to advanced symptoms of ALS. We found that phonemes, words, and higher order language units could be decoded well above chance. While sentence decoding accuracy was below that of demonstrations in participants with dysarthria, we attained an extensive characterization of neural signals underlying speech in a person with locked-in syndrome and identify directions for future improvement. These include closed-loop speech imagery training and decoding linguistic (rather than phonemic) units from neural signals in middle precentral gyrus to augment decoding at the sentence level. These results demonstrate that usable speech decoding from motor cortex may be feasible in people with anarthria and ventilator dependence.

Cell ReportsVol. 45(4)
Harvard University (US), Brown University (US), Providence VA Medical Center (US), Massachusetts General Hospital (US), Center for Neuro-Oncology (US), Neurological Surgery (US), University of California, Davis (US)
Burroughs Wellcome Fund, American Heart Association, A.P. Giannini Foundation, Rehabilitation Research and Development Service, National Institute on Deafness and Other Communication Disorders, National Institute of Neurological Disorders and Stroke
Openalex Percentile: Top 35%
EEG and Brain-Computer Interfaces
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