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Functional localization of audiovisual speech using near infrared spectroscopy
- Source :
- Brain Topography. 35:416-430
- Publication Year :
- 2022
- Publisher :
- Springer Science and Business Media LLC, 2022.
-
Abstract
- Visual cues are especially vital for hearing impaired individuals such as cochlear implant (CI) users to understand speech in noise. Functional Near Infrared Spectroscopy (fNIRS) is a light-based imaging technology that is ideally suited for measuring the brain activity of CI users due to its compatibility with both the ferromagnetic and electrical components of these implants. In a preliminary step toward better elucidating the behavioral and neural correlates of audiovisual (AV) speech integration in CI users, we designed a speech-in-noise task and measured the extent to which 24 normal hearing individuals could integrate the audio of spoken monosyllabic words with the corresponding visual signals of a female speaker. In our behavioral task, we found that audiovisual pairings provided average improvements of 103% and 197% over auditory-alone listening conditions in −6 and −9 dB signal-to-noise ratios consisting of multi-talker background noise. In an fNIRS task using similar stimuli, we measured activity during auditory-only listening, visual-only lipreading, and AV listening conditions. We identified cortical activity in all three conditions over regions of middle and superior temporal cortex typically associated with speech processing and audiovisual integration. In addition, three channels active during the lipreading condition showed uncorrected correlations associated with behavioral measures of audiovisual gain as well as with the McGurk effect. Further work focusing primarily on the regions of interest identified in this study could test how AV speech integration may differ for CI users who rely on this mechanism for daily communication.
Details
- ISSN :
- 15736792 and 08960267
- Volume :
- 35
- Database :
- OpenAIRE
- Journal :
- Brain Topography
- Accession number :
- edsair.doi.dedup.....efbfb1feb275a794be7c7625b9c691ef