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Hearing01:31

Hearing

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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.
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Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique
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基于深度学习的听觉注意力解码在听力受损的听众中

M Asjid Tanveer1, Martin A Skoglund2,3, Bo Bernhardsson1

  • 1Department of Automatic Control, Lund University, Lund, Sweden.

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|May 10, 2024
PubMed
概括

这项研究开发了用于快速听觉注意力解码 (AAD) 的深度学习模型,使用电脑电图 (EEG) 在有听力障碍 (HI) 的个体中. 这些模型有效地区分了语音,其方向和助听器状态,显示了临床应用的前景.

关键词:
这是一个EEGEEGEEGEEGEEGEEGEEG.听觉注意力解码的解码深度卷积神经网络是一个深度卷积神经网络.深度学习是一种深度学习.听力损伤 听力损伤跨/内部试验试验.

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科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 听觉注意力解码 (AAD) 对于在杂环境中理解语音感知至关重要.
  • 听力障碍 (HI) 患者在选择性听觉注意力方面面临重大挑战.
  • 电脑电图 (EEG) 提供了与听觉处理相关的神经活动的非侵入性测量.

研究的目的:

  • 开发一种用于快速AAD的深度学习 (DL) 方法,使用HI参与者的EEG数据.
  • 评估DL模型在三个关键分类任务上的表现:语音与噪音对比,语音方向和助听器状态.
  • 调查不同数据分割策略对AAD模型性能的影响.

主要方法:

  • 深度卷积神经网络 (DCNN) 模型被设计用于听觉注意力解码.
  • 用两种数据分割策略,试验间和试验内,来评估模型的概括性.
  • 来自31名HI参与者的EEG数据使用1秒分类窗口进行了分析.

主要成果:

  • 使用试验间策略,DCNN模型在所有三项任务中都实现了显著的准确性和曲线下的面积 (AUC).
  • 试验内部策略产生了更高的性能指标,这表明潜在的过拟合或膨胀的结果.
  • 模型证明了短,1秒的EEG段的有效性,表明适合实时应用.

结论:

  • 深度学习模型可以在HI患者中使用短EEG窗口成功执行听觉注意力解码任务.
  • 适当的数据分割对于基于EEG的AAD模型的可靠评估至关重要,试验间方法显示了更现实的性能.
  • 这些发现突显了基于EEG的工具在推进听觉技术和临床评估听觉注意力的潜力.