TY - JOUR
T1 - Stability of ECoG high gamma signals during speech and implications for a speech BCI system in an individual with ALS
T2 - a year-long longitudinal study
AU - Wyse Sookoo, Kimberley
AU - Luo, Shiyu
AU - Candrea, Daniel
AU - Schippers, Anouck
AU - Tippett, Donna C
AU - Wester, Brock
AU - Fifer, Matthew S
AU - Vansteensel, Mariska J
AU - Ramsey, Nick F
AU - Crone, Nathan
N1 - Publisher Copyright:
© 2024 IOP Publishing Ltd.
PY - 2024/8/1
Y1 - 2024/8/1
N2 - Objective. Speech brain-computer interfaces (BCIs) have the potential to augment communication in individuals with impaired speech due to muscle weakness, for example in amyotrophic lateral sclerosis (ALS) and other neurological disorders. However, to achieve long-term, reliable use of a speech BCI, it is essential for speech-related neural signal changes to be stable over long periods of time. Here we study, for the first time, the stability of speech-related electrocorticographic (ECoG) signals recorded from a chronically implanted ECoG BCI over a 12 month period. Approach. ECoG signals were recorded by an ECoG array implanted over the ventral sensorimotor cortex in a clinical trial participant with ALS. Because ECoG-based speech decoding has most often relied on broadband high gamma (HG) signal changes relative to baseline (non-speech) conditions, we studied longitudinal changes of HG band power at baseline and during speech, and we compared these with residual high frequency noise levels at baseline. Stability was further assessed by longitudinal measurements of signal-to-noise ratio, activation ratio, and peak speech-related HG response magnitude (HG response peaks). Lastly, we analyzed the stability of the event-related HG power changes (HG responses) for individual syllables at each electrode. Main Results. We found that speech-related ECoG signal responses were stable over a range of syllables activating different articulators for the first year after implantation. Significance. Together, our results indicate that ECoG can be a stable recording modality for long-term speech BCI systems for those living with severe paralysis. Clinical Trial Information. ClinicalTrials.gov, registration number NCT03567213.
AB - Objective. Speech brain-computer interfaces (BCIs) have the potential to augment communication in individuals with impaired speech due to muscle weakness, for example in amyotrophic lateral sclerosis (ALS) and other neurological disorders. However, to achieve long-term, reliable use of a speech BCI, it is essential for speech-related neural signal changes to be stable over long periods of time. Here we study, for the first time, the stability of speech-related electrocorticographic (ECoG) signals recorded from a chronically implanted ECoG BCI over a 12 month period. Approach. ECoG signals were recorded by an ECoG array implanted over the ventral sensorimotor cortex in a clinical trial participant with ALS. Because ECoG-based speech decoding has most often relied on broadband high gamma (HG) signal changes relative to baseline (non-speech) conditions, we studied longitudinal changes of HG band power at baseline and during speech, and we compared these with residual high frequency noise levels at baseline. Stability was further assessed by longitudinal measurements of signal-to-noise ratio, activation ratio, and peak speech-related HG response magnitude (HG response peaks). Lastly, we analyzed the stability of the event-related HG power changes (HG responses) for individual syllables at each electrode. Main Results. We found that speech-related ECoG signal responses were stable over a range of syllables activating different articulators for the first year after implantation. Significance. Together, our results indicate that ECoG can be a stable recording modality for long-term speech BCI systems for those living with severe paralysis. Clinical Trial Information. ClinicalTrials.gov, registration number NCT03567213.
KW - ECoG
KW - brain-computer interfaces
KW - neural signals
UR - http://www.scopus.com/inward/record.url?scp=85198681496&partnerID=8YFLogxK
U2 - 10.1088/1741-2552/ad5c02
DO - 10.1088/1741-2552/ad5c02
M3 - Article
C2 - 38925110
SN - 1741-2560
VL - 21
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
IS - 4
M1 - 046016
ER -