Effects of Silent Intervals on the Extraction of Human Frequency-Following Responses Using Non-Negative Matrix Factorization.

Source-Separation Non-Negative Matrix Factorization (SSNMF) is a mathematical algorithm recently developed to extract scalp-recorded frequency-following responses (FFRs) from noise. Despite its initial success, the effects of silent intervals on algorithm performance remain undetermined. Our purpose...

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Detalles Bibliográficos

Publicado en:	Perceptual & Motor Skills Vol. 130; no. 5; pp. 1834 - 1852
Autores principales:	Giordano, Allison T., Jeng, Fuh-Cherng, Black, Taylor R., Bauer, Sydney W., Carriero, Amanda E., McDonald, Kalyn, Lin, Tzu-Hao, Wang, Ching-Yuan
Formato:	Artículo
Publicado:	Sage Publications Inc. Oct2023
Materias:	Auditory evoked response Physiological aspects of speech Electroencephalography Neuroplasticity Machine learning Electrophysiology Brain stem Algorithms
Acceso en línea:	Ver este registro en EBSCOhost