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通过基于EEG的连续语音外重建来估计语音接收值.

Heidi B Borges1,2, Johannes Zaar1,3, Emina Alickovic1,4

  • 1Eriksholm Research Centre, Snekkersten, Denmark.

The European journal of neuroscience
|March 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究使用脑电图 (EEG) 和语音外重建来估计语音接收值 (SRT). 结果显示基于EEG的SRT估计是准确的,并受到信号噪声比 (SNR) 的影响,影响神经反应.

关键词:
EEG 的措施是 EEG 的措施.包裹跟踪的跟踪方式神经解码的神经解码在噪音中的言语语音的可理解性 语音的可理解性

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科学领域:

  • 神经科学是一个神经科学.
  • 听觉神经科学 听觉神经科学
  • 信号处理 信号处理

背景情况:

  • 准确估计语音接收值 (SRT) 对于诊断听力障碍至关重要.
  • 电脑电图 (EEG) 提供了一种非侵入性的方法来研究听觉处理.
  • 来自EEG的语音外重建提供了对语音神经编码的洞察.

研究的目的:

  • 为了研究基于EEG的语音外重建来估计SRT (SRTneuro).
  • 检查信号与噪声比 (SNR) 对听觉处理中的时间响应函数 (TRF) 的影响.
  • 为了将SNR对外重建和TRF的影响与潜在的神经机制相关联.

主要方法:

  • 记录了20名正常听力参与者的EEG,在不同噪音水平下听到连续的语音.
  • 训练了一台线性解码器,从EEG中重建语音包裹.
  • 计算的重建精度 (皮尔森相关性) 和估计的SRTneuro.
  • 在不同的SNR中分析了TRF组件 (延迟,振幅).

主要成果:

  • 对所有参与者来说,SRT神经估计在行为SRT的3dB以内.
  • 增加SNR显著降低了TRF组件延迟 (N1,P2).
  • 增加SNR显著增加了TRF组件振幅大小 (N1,P2).

结论:

  • 基于EEG的语音外重建是SRT估计的可行方法.
  • SNR对语音外重建的准确性和TRF特征都有重大影响.
  • 这些发现表明,SNR对包裹编码和TRF调制有共同的神经基础.