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

doi:10.1002/advs.202304853

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

Adv Sci (Weinh). 2023 Dec;10(35):e2304853. doi: 10.1002/advs.202304853. Epub 2023 Oct 24.

Authors

Shiyu Luo¹, Miguel Angrick², Christopher Coogan², Daniel N Candrea¹, Kimberley Wyse-Sookoo¹, Samyak Shah², Qinwan Rabbani^{3

4}, Griffin W Milsap⁵, Alexander R Weiss², William S Anderson⁶, Donna C Tippett^{2

7

8}, Nicholas J Maragakis², Lora L Clawson², Mariska J Vansteensel⁹, Brock A Wester⁵, Francesco V Tenore⁵, Hynek Hermansky^{3

4}, Matthew S Fifer⁵, Nick F Ramsey⁹, Nathan E Crone²

Affiliations

¹ Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
² Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
³ Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
⁴ Center for Language and Speech Processing, Johns Hopkins University, Baltimore, MD, 21218, USA.
⁵ Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, 20723, USA.
⁶ Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
⁷ Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
⁸ Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
⁹ Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, 3584, The Netherlands.

Abstract

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.

Keywords: amyotrophic lateral sclerosis (ALS); brain-computer interfaces; neural decoding; speech brain-computer interface (BCI).

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Amyotrophic Lateral Sclerosis* / complications
Brain-Computer Interfaces*
Electrocorticography
Humans
Speech

Abstract

Publication types

MeSH terms

Grants and funding