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Updated: Oct 20, 2025

Quantitative Assessment of Cortical Auditory-tactile Processing in Children with Disabilities
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Controlling test specificity for auditory evoked response detection using a frequency domain bootstrap.

M A Chesnaye1, S L Bell1, J M Harte2

  • 1Institute of Sound and Vibration Research, Faculty of Engineering and the Environment, University of Southampton, UK.

Journal of Neuroscience Methods
|September 11, 2021
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Summary
This summary is machine-generated.

Frequency domain bootstrap (FDB) methods improve auditory evoked response (AER) detection by generating accurate null distributions. Modified FDB approaches enhance control over false-positive rates in non-stationary AER data.

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Area of Science:

  • Auditory Neuroscience
  • Biomedical Signal Processing
  • Statistical Inference

Background:

  • Statistical detection methods are standard for automating auditory evoked response (AER) measurements.
  • Violated statistical assumptions in AER data can compromise the performance of these detection methods.
  • This study investigates frequency domain bootstrap (FDB) and its modifications for reliable AER detection in complex data.

Purpose of the Study:

  • To evaluate the efficacy of frequency domain bootstrap (FDB) methods for auditory evoked response (AER) detection.
  • To address limitations of conventional FDB in handling serially correlated, non-stationary AER data.
  • To improve the control of false-positive rates in AER detection.

Main Methods:

  • Exploration of a frequency domain bootstrap (FDB) approach and two modifications.
  • Generation of surrogate recordings to mimic the serial correlation of original AER data.
  • Application of Hotelling's T-squared (HT2) test to auditory brainstem responses as a case study.

Main Results:

  • Conventional Hotelling's T2 (HT2) tests showed significant deviations in false-positive rates due to serial correlation.
  • Modified FDB approaches demonstrated false-positive rates closer to nominal alpha-levels.
  • Improved performance of modified FDB was notable in heteroskedastic data and data with non-smooth power spectral density functions.

Conclusions:

  • FDB and its modifications offer accurate, recording-specific approximations of null distributions for AER detection.
  • These methods provide superior control over false-positive rates compared to traditional parametric inference.
  • The findings support the use of modified FDB for robust AER analysis, particularly in challenging datasets.