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方向性声音处理和听者动机对连续噪声语音的 EEG 反应的影响:正常听力和助听听力障碍者是否不同?

Effects of directional sound processing and listener's motivation on EEG responses to continuous noisy speech: Do normal-hearing and aided hearing-impaired listeners differ?

机构信息

Department of Psychology, University of Oldenburg, Ammerländer Heerstraße 114, 26129, Oldenburg, Germany; Cluster of Excellence "Hearing4all", Oldenburg, Germany.

Department of Psychology, University of Oldenburg, Ammerländer Heerstraße 114, 26129, Oldenburg, Germany.

出版信息

Hear Res. 2019 Jun;377:260-270. doi: 10.1016/j.heares.2019.04.005. Epub 2019 Apr 11.

Abstract

OBJECTIVE

It has been suggested that the next major advancement in hearing aid (HA) technology needs to include cognitive feedback from the user to control HA functionality. In order to enable automatic brainwave-steered HA adjustments, attentional processes underlying speech-in-noise perception in aided hearing-impaired individuals need to be better understood. Here, we addressed the influence of two important factors for the listening performance of HA users - hearing aid processing and motivation - by analysing ongoing neural responses during long-term listening to continuous noisy speech.

METHODS

Sixteen normal-hearing (NH) and 15 linearly aided hearing-impaired (aHI) participants listened to an audiobook recording embedded in realistic speech babble noise at individually adjusted signal-to-noise ratios (SNRs). A HA simulator was used for simulating a directional microphone setting as well as for providing individual amplification. To assess listening performance behaviourally, participants answered questions about the contents of the audiobook. We manipulated (1) the participants' motivation by offering a monetary reward for good listening performance in one half of the measurements and (2) the SNR by engaging/disengaging the directional microphone setting. During the speech-in-noise task, electroencephalography (EEG) signals were recorded using wireless, mobile hardware. EEG correlates of listening performance were investigated using EEG impulse responses, as estimated using the cross-correlation between the recorded EEG signal and the temporal envelope of the audiobook at the output of the HA simulator.

RESULTS

At the behavioural level, we observed better performance for the NH listeners than for the aHI listeners. Furthermore, the directional microphone setting led to better performance for both participant groups, and when the directional microphone setting was disengaged motivation also improved the performance of the aHI participants. Analysis of the EEG impulse responses showed faster N1P2 responses for both groups and larger N2 peak amplitudes for the aHI group when the directional microphone setting was activated, but no physiological correlates of motivation.

SIGNIFICANCE

The results of this study indicate that motivation plays an important role for speech understanding in noise. In terms of neuro-steered HAs, our results suggest that the latency of attentional processes is influenced by HA-induced stimulus changes, which can potentially be used for inferring benefit from noise suppression processing automatically. Further research is necessary to identify the neural correlates of motivation as an exclusive top-down process and to combine such features with HA-driven ones for online HA adjustments.

摘要

目的

有人认为,助听器(HA)技术的下一个重大进展需要包括来自用户的认知反馈来控制 HA 功能。为了实现自动脑波引导的 HA 调整,需要更好地理解听觉障碍个体在助听噪声环境下言语感知的注意力过程。在这里,我们通过分析长期聆听连续嘈杂语音时的持续神经反应,研究了对 HA 用户听力表现有重要影响的两个因素 - 助听器处理和动机。

方法

16 名正常听力(NH)和 15 名线性辅助听力障碍(aHI)参与者在个人调整的信噪比(SNR)下聆听嵌入真实语音背景噪声的有声读物录音。使用助听器模拟器模拟定向麦克风设置以及提供个性化放大。为了从行为上评估听力表现,参与者回答有关有声读物内容的问题。我们通过在一半测量中提供良好听力表现的金钱奖励来操纵(1)参与者的动机,以及(2)通过启用/禁用定向麦克风设置来操纵 SNR。在语音噪声任务中,使用无线移动硬件记录脑电图(EEG)信号。使用 EEG 脉冲响应来研究 EEG 与听力表现的相关性,该响应是使用记录的 EEG 信号与助听器模拟器输出处有声读物的时间包络之间的互相关估计得出的。

结果

在行为层面,我们观察到 NH 听众的表现优于 aHI 听众。此外,定向麦克风设置使两个参与者群体的表现都更好,当定向麦克风设置被停用后,动机也提高了 aHI 参与者的表现。对 EEG 脉冲响应的分析表明,对于两个组,当启用定向麦克风设置时,N1P2 反应更快,并且 aHI 组的 N2 峰值幅度更大,但没有与动机相关的生理相关性。

意义

这项研究的结果表明,动机在噪声中的言语理解中起着重要作用。就神经引导的 HA 而言,我们的结果表明,注意力过程的潜伏期受 HA 引起的刺激变化的影响,这可能被用于自动推断噪声抑制处理的受益。需要进一步研究以确定动机的神经相关性作为一种单独的自上而下的过程,并将这些特征与 HA 驱动的特征结合起来用于在线 HA 调整。

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