Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months

Shiyu Luo, Miguel Angrick, Christopher Coogan, Daniel N. Candrea, Kimberley Wyse-Sookoo, Samyak Shah, Qinwan Rabbani, Griffin W. Milsap, Alexander R. Weiss, William S. Anderson, Donna C. Tippett, Nicholas J. Maragakis, Lora L. Clawson, Mariska J. Vansteensel, Brock A. Wester, Francesco V. Tenore, Hynek Hermansky, Matthew S. Fifer, Nick F. Ramsey, Nathan E. Crone*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Brain-computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex. Speech commands are accurately detected and decoded (median accuracy: 90.59%) throughout a 3-month study period without model retraining or recalibration. Use of the BCI does not require exogenous timing cues, enabling the participant to issue self-paced commands at will. These results demonstrate that a chronically implanted ECoG-based speech BCI can reliably control assistive devices over long time periods with only initial model training and calibration, supporting the feasibility of unassisted home use.

Original languageEnglish
Article number2304853
JournalAdvanced Science
Issue number35
Publication statusPublished - 15 Dec 2023


  • amyotrophic lateral sclerosis (ALS)
  • brain-computer interfaces
  • neural decoding
  • speech brain-computer interface (BCI)


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