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Detecting Human Frequency-Following Responses Using an Artificial Neural Network.

Fuh-Cherng Jeng1, Amanda E Carriero1, Sydney W Bauer1

  • 1Hearing, Speech, and Language Sciences, Ohio University, Athens, OH, USA.

Perceptual and Motor Skills
|May 29, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning models show promise for analyzing neural signals like frequency-following responses (FFRs). An artificial neural network achieved 84% accuracy in detecting FFRs, aiding auditory processing research.

Keywords:
artificial neural networkelectroencephalogramfrequency-following responseintonationmachine learningmodel performance

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Area of Science:

  • Neuroscience
  • Auditory Neuroscience
  • Computational Neuroscience

Background:

  • Frequency-following responses (FFRs) are neural signals reflecting the brain's encoding of acoustic features, crucial for speech perception.
  • Traditional machine learning has been applied to FFRs, but deep learning's potential is largely unexplored.

Purpose of the Study:

  • To investigate the efficacy of a three-layer artificial neural network (ANN) for detecting the presence or absence of FFRs.
  • To assess ANN performance in classifying FFRs elicited by a rising intonation of the English vowel /i/.

Main Methods:

  • ANN model trained and tested on FFR recordings.
  • Input data comprised F0 estimates from the spectral domain.
  • Model performance evaluated by varying inputs, hidden neurons, and sweep counts.

Main Results:

  • ANN prediction accuracy was significantly influenced by the number of inputs, hidden neurons, and sweeps.
  • Optimal configurations involved 6-8 inputs and 4-6 hidden neurons.
  • Approximately 84% prediction accuracy was achieved with 100+ sweeps, enhancing signal-to-noise ratio.

Conclusions:

  • Deep learning, specifically ANNs, can effectively detect FFRs.
  • Optimized ANN models offer a promising tool for auditory processing assessments.
  • This approach lays groundwork for advancements in clinical diagnostics related to auditory function.