Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

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

Perceptual and motor skills·2025
Same author

Advancing Auditory Processing by Detecting Frequency-Following Responses Through a Specialized Machine Learning Model.

Perceptual and motor skills·2023
Same author

Effects of Silent Intervals on the Extraction of Human Frequency-Following Responses Using Non-Negative Matrix Factorization.

Perceptual and motor skills·2023
Same author

Separating the Novel Speech Sound Perception of Lexical Tone Chimeras From Their Auditory Signal Manipulations: Behavioral and Electroencephalographic Evidence.

Perceptual and motor skills·2021

Related Experiment Video

Updated: Jun 16, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

427

Machine Learning Recognizes Frequency-Following Responses in American Adults: Effects of Reference Spectrogram and

Sydney W Bauer1, Fuh-Cherng Jeng1, Amanda Carriero1

  • 1Communication Sciences and Disorders, Ohio University, Athens, OH, USA.

Perceptual and Motor Skills
|August 16, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning significantly improved brainstem frequency-following responses (FFRs) when trained with individual or averaged brain activity patterns. This enhances electrophysiological analysis for potential clinical applications.

Keywords:
acoustic stimuliauditory electrophysiologyfrequency-following responsemachine learningspectrogram

More Related Videos

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.5K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.4K

Related Experiment Videos

Last Updated: Jun 16, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

427
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.5K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.4K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Electrophysiology studies brain responses to sound.
  • Frequency-following response (FFR) is a key measure of auditory encoding.
  • Limitations exist in current FFR recording and analysis.

Purpose of the Study:

  • To enhance frequency-following responses (FFRs) using an improved source-separation machine learning algorithm.
  • Investigate the efficacy of a specific machine learning algorithm (SSNMF) for FFR enhancement.
  • Assess the impact of different training references on FFR quality.

Main Methods:

  • Recruited 28 normal-hearing native English speakers.
  • Recorded electroencephalographic (EEG) signals during auditory stimulation (/i/ and /da/ tokens).
  • Applied a source-separation non-negative matrix factorization (SSNMF) algorithm, trained with individual, grand-averaged, or stimulus token spectrograms.

Main Results:

  • FFRs were significantly enhanced (p < .001) when SSNMF was trained using individual and grand-averaged spectrograms.
  • No significant enhancement was observed when training with stimulus token spectrograms.
  • Similar enhancement patterns were found for both /i/ and /da/ stimulus tokens.

Conclusions:

  • The SSNMF machine learning algorithm effectively enhances FFRs when trained appropriately.
  • Training with individual and grand-averaged spectrograms is crucial for FFR improvement.
  • This advancement holds promise for improving FFR obtainment, analysis, and clinical utility.