Machine learning in detecting frequency-following responses

Hart, Breanna N.; Jeng, Fuh-Cherng

doi:10.1121/2.0000931

Cited by 3 publications

(4 citation statements)

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“…64 Due to the large number of viable computational algorithms, it is impractical to include all of them in one article. As such, this article focuses on the algorithms and models that have been reported in the recent FFR literature 20 34 62 65 66 67 68 69 70 71 or which, in the authors' opinion, may be useful in furthering FFR research. To begin with, three basic ML terminologies are defined as the following:…”

Section: Machine Learningmentioning

confidence: 99%

“…Applicability : This model has been successfully implemented in FFR research. 20 62 66 68 69 Although the SVM model is originally intended for binary classification, it can be easily extended to multiclass classification. A soft margin can be used to widen the margin and thus enhance separation results.…”

Section: Machine Learningmentioning

confidence: 99%

“…One common utility of ML models is its ability to classify FFRs into appropriate categories for a specific clinical population. For example, a selection of 23 ML models have been used to successfully identify the presence or absence of an FFR in normal-hearing adults 69 and neonates. 66 An HMM can successfully classify FFRs in response to the four Mandarin tones (/yi 1 /, /yi 2 /, /yi 3 /, and /yi 4 /) for American and Chinese adults with ≥77% prediction accuracies, 67 and can also be customized to differentiate the listener's identity (American or Chinese) with success.…”

Section: Technical and Clinical Benefits Of Machine Learningmentioning

confidence: 99%

“…To date, a wide spectrum of ML techniques has been used on several FFR projects. 20 34 62 65 66 67 68 69 70 71 Table 1 provides a summary of those projects with an emphasis on their ML-related research questions, model designs, approaches, and findings.…”

Section: Technical and Clinical Benefits Of Machine Learningmentioning

confidence: 99%

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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: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Section: Technical and Clinical Benefits Of Machine Learningmentioning

confidence: 99%

Section: Technical and Clinical Benefits Of Machine Learningmentioning

confidence: 99%

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

Jeng

2022

Semin Hear

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Non-negative matrix factorization improves the efficiency of recording frequency-following responses in normal-hearing adults and neonates

Jeng

Lin

Hart

et al. 2022

International Journal of Audiology

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A Demonstration of Machine Learning in Detecting Frequency Following Responses in American Neonates

Hart

Jeng

2020

Percept Mot Skills

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In this study, we sought to evaluate the efficiencies of multiple machine learning algorithms in detecting neonates’ Frequency Following Responses (FFRs). We recorded continuous brainwaves from 43 American neonates in response to a pre-recorded monosyllable/i/with a rising frequency contour. Recordings were classified into response and no response categories. Six response features were extracted from each recording and served as predictors in FFR identification. Twenty-three supervised machine learning algorithms with mean efficiency values of 86.0%, 94.4%, 97.2%, and 97.5% when 1, 10, 100, and 1000 random iterations were implemented, respectively. These high efficiency values obtained from the neonatal FFRs demonstrate that machine learning algorithms can help assess pitch processing in neonates and can be applied to auditory screening and intervention services for neonates at risk for disorders associated with decreased pitch processing.

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Machine learning in detecting frequency-following responses

Cited by 3 publications

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

Non-negative matrix factorization improves the efficiency of recording frequency-following responses in normal-hearing adults and neonates

A Demonstration of Machine Learning in Detecting Frequency Following Responses in American Neonates

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