Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Hearing01:31

Hearing

52.3K
When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
52.3K
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

215
The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
215
The Cochlea01:13

The Cochlea

45.0K
The cochlea is a coiled structure in the inner ear that contains hair cells—the sensory receptors of the auditory system. Sound waves are transmitted to the cochlea by small bones attached to the eardrum called the ossicles, which vibrate the oval window that leads to the inner ear. This causes fluid in the chambers of the cochlea to move, vibrating the basilar membrane.
45.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

EBV strain interacts with host HLA to drive nasopharyngeal carcinoma risk.

Nature·2026
Same author

The Role of Live and Dead Corals in Shaping Fish Assemblages Across Life Stages.

Ecology and evolution·2025
Same author

The Effect of the Nasal Airflow Reducer on Parasympathetic Activity in Adults: A Pilot and Exploratory Study.

Medicina (Kaunas, Lithuania)·2025
Same author

Optimizing Non-Invasive PGT-A: A Multi-Factorial Approach for Enhanced Accuracy and Seamless Integration Into Clinical IVF.

Reproductive medicine and biology·2025
Same author

Environmental and acoustic drivers of fish recruitment along degraded coral reefs.

Marine environmental research·2025
Same author

Machine learning-based system for online quantitative monitoring of heavy metals across different aqueous matrices using spectroscopy of plasmas in liquids.

Talanta·2025

相关实验视频

Updated: Jul 6, 2025

Infant Auditory Processing and Event-related Brain Oscillations
06:34

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

16.4K

通过通过专门的机器学习模型检测频率后续响应来推进听觉处理.

Fuh-Cherng Jeng1,2, Katie Matzdorf1, Kassy L Hickman1

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

Perceptual and motor skills
|December 28, 2023
PubMed
概括
此摘要是机器生成的。

这项研究开发了一个专门的机器学习 (ML) 模型,使用源分离非负矩阵分解 (SSNMF) 来改进频率跟踪响应 (FFR) 的检测. 基于SSNMF的ML模型在识别听觉处理信号方面表现出更高的准确性和效率.

关键词:
这是一个EEGEEGEEGEEGEEGEEGEEG.接下来的频率响应响应.词汇语调的词汇语调.机器学习是机器学习.模型的性能模型的性能.非负矩阵因子化的非负矩阵因子化.

更多相关视频

Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach
10:50

Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach

Published on: June 6, 2012

14.6K
Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
06:01

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

2.5K

相关实验视频

Last Updated: Jul 6, 2025

Infant Auditory Processing and Event-related Brain Oscillations
06:34

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

16.4K
Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach
10:50

Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach

Published on: June 6, 2012

14.6K
Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
06:01

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

2.5K

科学领域:

  • 听觉神经科学 听觉神经科学
  • 医疗保健中的机器学习
  • 信号处理 信号处理

背景情况:

  • 频率跟踪响应 (FFR) 对于评估听觉处理至关重要.
  • 由于背景噪音和有限的数据,检测FFR可能具有挑战性.
  • 现有的方法可能需要大量的数据或与噪音信号作斗争.

研究的目的:

  • 评估一种专门的机器学习 (ML) 模型,用于检测头皮记录的频率跟踪响应 (FFR).
  • 为了利用源分离非负矩阵分解 (SSNMF) 算法来增强FFR检测.
  • 为了评估模型在噪音条件下和不同数量的记录扫描时的性能.

主要方法:

  • 通过调整SSNMF算法用于FFR检测,开发了一种混合ML模型.
  • 招募了40名听力正常的成年人,并使用英语母音 /i/ 唤起了FFR.
  • 在FFR存在和FFR缺席条件下训练模型,评估灵敏度,特异性和效率.

主要成果:

  • 专门的SSNMF ML模型显示,随着记录扫描的增加,提高了灵敏度,特异性和效率.
  • 在500次扫描时,灵敏度超过80%,从1000次扫描开始,灵敏度超过89%.
  • 具体性和效率也随着扫描数量的增加而迅速改善.

结论:

  • 专门的SSNMF ML模型对于检测FFR是实用且有效的,即使数据和背景噪声有限.
  • 随着更多的记录扫描,该模型的性能显著提高,这表明其坚固性.
  • 这些发现支持这种ML模型在FFR研究和临床声学中用于听觉处理评估的更广泛应用.