| Paper: | PS-2B.45 | ||
| Session: | Poster Session 2B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Using EEG to Predict Speech Intelligibility | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1315-0 | ||
| Authors: | Ivan Iotzov, Lucas Parra, City University of New York, United States | ||
| Abstract: | Speech signals have the ability to entrain brain activity to the rapid fluctuations found in speech sounds. This entrainment can be measured using electroencephalographic (EEG) recordings and is strong enough to allow discrimination between attended and unattended speech sources. In this study, we investigated whether these entrainment responses can be used to measure how intelligible a speech signal is to a subject. We hypothesized that when intelligibility is degraded, attention wanes and the stimulus-response correlation will decrease. To test this, we measured a listener's ability to detect words in noisy, natural speech while recording brain activity using EEG. We altered intelligibility by presenting congruent or incongruent video of the speaker along with their speech. For almost all subjects, word detection performance improved in the congruent condition and this improvement coincided with an increase in stimulus-response correlation. We conclude that simultaneous recordings of perceived sound and EEG activity may represent a practical tool to assess speech intelligibility, specifically in the context of hearing aid devices. | ||