Real-time classification of auditory sentences using evoked cortical activity in humans.
J Neural Eng
; 15(3): 036005, 2018 06.
Article
en En
| MEDLINE
| ID: mdl-29378977
OBJECTIVE: Recent research has characterized the anatomical and functional basis of speech perception in the human auditory cortex. These advances have made it possible to decode speech information from activity in brain regions like the superior temporal gyrus, but no published work has demonstrated this ability in real-time, which is necessary for neuroprosthetic brain-computer interfaces. APPROACH: Here, we introduce a real-time neural speech recognition (rtNSR) software package, which was used to classify spoken input from high-resolution electrocorticography signals in real-time. We tested the system with two human subjects implanted with electrode arrays over the lateral brain surface. Subjects listened to multiple repetitions of ten sentences, and rtNSR classified what was heard in real-time from neural activity patterns using direct sentence-level and HMM-based phoneme-level classification schemes. MAIN RESULTS: We observed single-trial sentence classification accuracies of [Formula: see text] or higher for each subject with less than 7 minutes of training data, demonstrating the ability of rtNSR to use cortical recordings to perform accurate real-time speech decoding in a limited vocabulary setting. SIGNIFICANCE: Further development and testing of the package with different speech paradigms could influence the design of future speech neuroprosthetic applications.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Corteza Auditiva
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Habla
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Percepción del Habla
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Estimulación Acústica
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Sistemas de Computación
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Interfaces Cerebro-Computador
Límite:
Humans
Idioma:
En
Revista:
J Neural Eng
Asunto de la revista:
NEUROLOGIA
Año:
2018
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Reino Unido