Contralateral noise degrades frequency-coding accuracy in normal-hearing adults – preliminary results

Jeng, Fuh-Cherng; Stehura, Kelley A.; Hart, Breanna N.; Giordano, Allison T.

doi:10.1121/2.0001516

Cited by 1 publication

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“…Within the auditory system, the FFR opens a window allowing careful examinations with microsecond precision on the brain's temporal and spectral processing of the consonant, 10 11 vowel, 12 13 intonation, 14 15 fundamental frequency, 16 17 harmonics, 12 18 and many other aspects of any incoming acoustic stimulus. 7 19 Beyond the auditory system, the FFR is a powerful tool investigating the influences of attention, 8 20 language experience, 21 22 musical training, 23 24 athletic condition, 25 concussion, 26 27 noise, 28 29 reverberation, 30 and aging 31 32 on the human brain. For patients with specific pathologies (auditory or nonauditory), the FFR is a perfect candidate to assess the overall health and integrity of the auditory pathways and beyond.…”

Section: Frequency-following Responsementioning

confidence: 99%

Implementation of Machine Learning on Human Frequency-Following Responses: A Tutorial

Jeng

2022

Semin Hear

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The frequency-following response (FFR) provides enriched information on how acoustic stimuli are processed in the human brain. Based on recent studies, machine learning techniques have demonstrated great utility in modeling human FFRs. This tutorial focuses on the fundamental principles, algorithmic designs, and custom implementations of several supervised models (linear regression, logistic regression, k-nearest neighbors, support vector machines) and an unsupervised model (k-means clustering). Other useful machine learning tools (Markov chains, dimensionality reduction, principal components analysis, nonnegative matrix factorization, and neural networks) are discussed as well. Each model's applicability and its pros and cons are explained. The choice of a suitable model is highly dependent on the research question, FFR recordings, target variables, extracted features, and their data types. To promote understanding, an example project implemented in Python is provided, which demonstrates practical usage of several of the discussed models on a sample dataset of six FFR features and a target response label.

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Section: Frequency-following Responsementioning

confidence: 99%

Implementation of Machine Learning on Human Frequency-Following Responses: A Tutorial

Jeng

2022

Semin Hear

View full text Add to dashboard Cite

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Contralateral noise degrades frequency-coding accuracy in normal-hearing adults – preliminary results

Cited by 1 publication

References 0 publications

Implementation of Machine Learning on Human Frequency-Following Responses: A Tutorial

Implementation of Machine Learning on Human Frequency-Following Responses: A Tutorial

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