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...
| Publicado en: | Perceptual & Motor Skills Vol. 130; no. 5; pp. 1834 - 1852 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
Oct2023
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=172825010&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 172825010 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00315125 PSK jtl: Perceptual & Motor Skills issn: 00315125 maglogo: Y pubinfo: dt: Oct2023 vid: 130 iid: 5 pid: 344 pub: Sage Publications Inc. artinfo: ui: 172825010 10.1177/00315125231191303 ppf: 1834 ppct: 18 formats: tig: atl: Effects of Silent Intervals on the Extraction of Human Frequency-Following Responses Using Non-Negative Matrix Factorization. aug: au: Giordano, Allison T. Jeng, Fuh-Cherng Black, Taylor R. Bauer, Sydney W. Carriero, Amanda E. McDonald, Kalyn Lin, Tzu-Hao Wang, Ching-Yuan affil: Communication Sciences and Disorders, 1354 Ohio University, Athens, Ohio, USA Biodiversity Research Center, 38017 Academia Sinica, Taipei, Taiwan Department of Otolaryngology-HNS, 38020 China Medical University Hospital, Taichung, Taiwan su: Auditory evoked response Physiological aspects of speech Electroencephalography Neuroplasticity Machine learning Electrophysiology Brain stem Algorithms sug: subj: Auditory evoked response Physiological aspects of speech Electroencephalography Neuroplasticity Machine learning Electrophysiology Brain stem Algorithms keyword: algorithm performance electroencephalographic frequency-following response lexical tone machine learning non-negative matrix factorization silent interval algorithm performance electroencephalographic frequency-following response lexical tone machine learning non-negative matrix factorization silent interval ab: 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 in this study was to determine the effects of silent intervals on the extraction of FFRs, which are electrophysiological responses that are commonly used to evaluate auditory processing and neuroplasticity in the human brain. We used an English vowel /i/ with a rising frequency contour to evoke FFRs in 23 normal-hearing adults. The stimulus had a duration of 150 ms, while the silent interval between the onset of one stimulus and the offset of the next one was also 150 ms. We computed FFR Enhancement and Noise Residue to estimate algorithm performance, while silent intervals were either included (i.e., the WithSI condition) or excluded (i.e., the WithoutSI condition) in our analysis. The FFR Enhancements and Noise Residues obtained in the WithoutSI condition were significantly better (p <.05) than those obtained in the WithSI condition. On average, the exclusion of silent intervals produced a 11.78% increment in FFR Enhancement and a 20.69% decrement in Noise Residue. These results not only quantify the effects of silent intervals on the extraction of human FFRs, but also provide recommendations for designing and improving the SSNMF algorithm in future research. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|