对比方法来推导听力脑干对人类听众连续演讲的响应
Tong Shan1, Ross K Maddox1,2,3
1Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States.
Imaging neuroscience (Cambridge, Mass.)
|August 13, 2025
概括
听觉脑干反应 (ABR) 可以通过时间响应函数 (TRF) 分析从语音中得出. 听觉神经模型 (ANM) 回归器与峰值语音为皮层下语音编码研究提供了最佳性能.
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
- 语音处理 语音处理
背景情况:
- 从连续的自然语音中获得听觉脑干反应 (ABR) 对于理解皮质下语音编码至关重要.
- 时间响应函数 (TRF) 分析,一种解卷法,将听觉通路模型作为一个线性系统.
研究的目的:
- 为了比较三个回归器的性能 - - 半波纠正刺激 (HWR),喉脉冲列车 (GP) 和听觉神经模拟响应 (ANM) - - 从自然和峰值语音中导出ABR.
- 根据信号与噪声比,预测准确性,效率和实用性来评估回归器.
主要方法:
- 分析了来自受试者听不变的自然语音和合成的尖峰语音的EEG录音.
- 使用HWR,GP和ANM回归器来得出TRF.
- 评估了性能指标,包括信号噪声比和预测准确性.
主要成果:
- 具有峰值演讲的ANM回归器产生了最佳表现.
- 语音不变的ANM和语音峰值的GP显示了可比的,强大的结果.
- 在各种条件下,HWR回归器表现相对较差.
结论:
- 多种刺激和分析工具可以为ABR导出高质量的皮下TRF.
- 回归器和语音类型的选择取决于具体的实验要求.
- 这些发现指导了ABR从自然主义演讲的未来研究和临床应用.
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