Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months.
amyotrophic lateral sclerosis (ALS)
brain-computer interfaces
neural decoding
speech brain-computer interface (BCI)
Journal
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
ISSN: 2198-3844
Titre abrégé: Adv Sci (Weinh)
Pays: Germany
ID NLM: 101664569
Informations de publication
Date de publication:
24 Oct 2023
24 Oct 2023
Historique:
revised:
18
09
2023
received:
17
07
2023
medline:
25
10
2023
pubmed:
25
10
2023
entrez:
24
10
2023
Statut:
aheadofprint
Résumé
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.
Identifiants
pubmed: 37875404
doi: 10.1002/advs.202304853
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2304853Subventions
Organisme : NIDCD NIH HHS
ID : U01DC016686
Pays : United States
Organisme : NINDS NIH HHS
ID : UH3NS114439
Pays : United States
Informations de copyright
© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.
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