Literature DB >> 30776785

EEG can predict speech intelligibility.

Ivan Iotzov1, Lucas C Parra.   

Abstract

OBJECTIVE: Speech signals have a remarkable ability to entrain brain activity to the rapid fluctuations of speech sounds. For instance, one can readily measure a correlation of the sound amplitude with the evoked responses of the electroencephalogram (EEG), and the strength of this correlation is indicative of whether the listener is attending to the speech. In this study we asked whether this stimulus-response correlation is also predictive of speech intelligibility. APPROACH: We hypothesized that when a listener fails to understand the speech in adverse hearing conditions, attention wanes and stimulus-response correlation also drops. To test this, we measure a listener's ability to detect words in noisy speech while recording their brain activity using EEG. We alter intelligibility without changing the acoustic stimulus by pairing it with congruent and incongruent visual speech. MAIN
RESULTS: For almost all subjects we found that an improvement in speech detection coincided with an increase in correlation between the noisy speech and the EEG measured over a period of 30 min. SIGNIFICANCE: We conclude that simultaneous recordings of the perceived sound and the corresponding EEG response may be a practical tool to assess speech intelligibility in the context of hearing aids.

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Mesh:

Year:  2019        PMID: 30776785     DOI: 10.1088/1741-2552/ab07fe

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  8 in total

1.  Speech Understanding Oppositely Affects Acoustic and Linguistic Neural Tracking in a Speech Rate Manipulation Paradigm.

Authors:  Eline Verschueren; Marlies Gillis; Lien Decruy; Jonas Vanthornhout; Tom Francart
Journal:  J Neurosci       Date:  2022-08-29       Impact factor: 6.709

2.  Neural Markers of Speech Comprehension: Measuring EEG Tracking of Linguistic Speech Representations, Controlling the Speech Acoustics.

Authors:  Marlies Gillis; Jonas Vanthornhout; Jonathan Z Simon; Tom Francart; Christian Brodbeck
Journal:  J Neurosci       Date:  2021-11-03       Impact factor: 6.709

3.  Prediction of Speech Intelligibility by Means of EEG Responses to Sentences in Noise.

Authors:  Jan Muncke; Ivine Kuruvila; Ulrich Hoppe
Journal:  Front Neurosci       Date:  2022-06-01       Impact factor: 5.152

4.  Neural responses to natural visual motion are spatially selective across the visual field, with selectivity differing across brain areas and task.

Authors:  Jason J Ki; Jacek P Dmochowski; Jonathan Touryan; Lucas C Parra
Journal:  Eur J Neurosci       Date:  2021-11-02       Impact factor: 3.698

5.  Neural Representation Enhanced for Speech and Reduced for Background Noise With a Hearing Aid Noise Reduction Scheme During a Selective Attention Task.

Authors:  Emina Alickovic; Thomas Lunner; Dorothea Wendt; Lorenz Fiedler; Renskje Hietkamp; Elaine Hoi Ning Ng; Carina Graversen
Journal:  Front Neurosci       Date:  2020-09-10       Impact factor: 4.677

6.  The interplay of top-down focal attention and the cortical tracking of speech.

Authors:  D Lesenfants; T Francart
Journal:  Sci Rep       Date:  2020-04-24       Impact factor: 4.379

7.  Intracorporeal Cortical Telemetry as a Step to Automatic Closed-Loop EEG-Based CI Fitting: A Proof of Concept.

Authors:  Andy J Beynon; Bart M Luijten; Emmanuel A M Mylanus
Journal:  Audiol Res       Date:  2021-12-13

8.  Three New Outcome Measures That Tap Into Cognitive Processes Required for Real-Life Communication.

Authors:  Thomas Lunner; Emina Alickovic; Carina Graversen; Elaine Hoi Ning Ng; Dorothea Wendt; Gitte Keidser
Journal:  Ear Hear       Date:  2020 Nov/Dec       Impact factor: 3.562

  8 in total

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