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Related Experiment Video

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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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Predicting speech intelligibility from a selective attention decoding paradigm in cochlear implant users.

Waldo Nogueira1, Hanna Dolhopiatenko1

  • 1Medical University Hannover, Cluster of Excellence 'Hearing4all', Hannover, Germany.

Journal of Neural Engineering
|March 2, 2022
PubMed
Summary
This summary is machine-generated.

Electroencephalography (EEG) decoding of selective attention in cochlear implant (CI) users is possible, even with single-ear speech. This EEG measure correlates with speech understanding, aiding objective assessment development.

Keywords:
cochlear implantelectroencephalographyneural trackingprediction speech understanding performanceselective attention

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Area of Science:

  • Neuroscience
  • Audiology
  • Biomedical Engineering

Background:

  • Cochlear implants (CIs) aim to restore hearing but often result in impaired speech understanding, especially in noisy environments.
  • Selective attention is crucial for speech comprehension in complex auditory scenes.
  • Electroencephalography (EEG) offers a non-invasive method to study neural correlates of auditory processing.

Purpose of the Study:

  • To investigate if EEG-based decoding of selective attention in cochlear implant (CI) users can predict speech understanding.
  • To assess the feasibility of decoding selective attention when speech streams are presented monaurally at different signal-to-interference ratios (SIRs).

Main Methods:

  • EEG data were recorded from CI users instructed to attend to one of two competing speech streams presented to the same ear.
  • Speech envelope reconstruction from EEG was performed using regularized least squares decoders.
  • Correlation coefficients (ρASIR, ρUSIR, ρDiff, ρDiffOpp) were computed to quantify attention decoding performance and its relation to speech understanding.

Main Results:

  • Selective attention decoding was achievable in CI users, even with monaural speech presentation.
  • Signal-to-interference ratio significantly impacted attention decoding metrics (ρASIR, ρDiff, ρDiffOpp), but not unattended speech decoding (ρUSIR).
  • Speech understanding performance correlated significantly with attention decoding metrics (ρASIR, ρUSIR, ρDiffOpp), with ρDiffOpp being less susceptible to CI artifacts.

Conclusions:

  • EEG-based selective attention decoding is feasible in CI users, offering a potential objective measure of speech understanding.
  • Careful consideration of CI artifacts and speech material is necessary for accurate decoder training.
  • These findings support the development of advanced, objective speech understanding assessments for CI users.