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验证一种新型范式,同时评估语音声音的不匹配响应和频率跟踪响应

Tzu-Han Zoe Cheng¹, Tian Christina Zhao¹

¹Department of Speech and Hearing Sciences, University of Washington, Seattle, WA 98195, USA; Institute for Learning & Brain Sciences, University of Washington, Seattle, WA 98195, USA.

Journal of neuroscience methods

|September 8, 2024

概括

这项研究引入了一种新的EEG范式,用于同时记录语音处理的不匹配响应 (MMR) 和频率跟踪响应 (FFR). 该方法有效地区分本地语音和非本地语音以及个别语音刺激.

科学领域:

神经科学是一个神经科学.
语音和语言处理语言处理
听觉神经科学听觉神经科学

背景情况:

语音处理涉及复杂的皮质和皮质下大脑结构.
不匹配响应 (MMR) 和频率跟踪响应 (FFR) 是语音处理的关键神经签名.
同时记录MMR和FFR是具有挑战性的,因为不同的先决条件.

研究的目的:

开发和验证用于并发MMR和FFR记录的新模式.
用MMR评估范式的能力,以区分母语与非母语的语言对比使用MMR.
为了评估范式在区分单个语音的可靠性,使用FFR.

主要方法:

在18名成年人中利用了脑电图 (EEG).
开发了一种用于同时获得MMR和FFR的新范式.
分析了MMR (原生与非原生) 和FFR (语音声音分化) 的解码精度.

主要成果:

为MMR实现了72.2%的解码精度,区分了母语和非母语语音对比.
在预期的时间窗口中观察到明显更大的本地MMR.
证明FFR的解码精度为79.6%,具有高的刺激-响应相关性,表明FFR跟踪语音声音密切.
FFR显示9毫秒的延迟,紧密跟踪语音声音.

结论:

这种新型范式可靠地同时捕获MMR和FFR.
与以前的研究相比,这种方法需要更少的试验和更短的实验时间.
这些发现有助于理解语音处理中的皮质-亚皮质相互作用,并有助于开发早期发育评估工具.

关键词:

频率跟踪响应的响应不匹配响应的反应不匹配.记录范式的记录方式语音感知语音感知

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