Personalized fitting and evaluation of hearing aids with EEG responses

通过脑电图反应对助听器进行个性化验配和评估

• 批准号: EP/M026728/1
• 负责人: Steven Bell
• 金额: $ 115.71万
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM026728%2F1
• 关键词: Personalized; fitting; evaluation; hearing; aids

It has been estimated that some 6 million people in the UK could benefit from hearing aids, but there are only approximately 2 million hearing aid users, and of these, only 70% use their hearing aids regularly. Modern hearing aids are complex devices with advanced features (gain in different frequency bands, amplitude compression, feedback cancellation, noise reduction, directional microphones etc.) and require professionals to fit them. Limited benefit from hearing aids is a major reason why many patients do not use their devices regularly. Conventionally, hearing aids are fitted based primarily on the 'audiogram', which informs on the quietest sounds (short tones) that the patient can hear at different frequencies and is obtained from patients' voluntary and subjective response (usually by clicking a button) to progressively quieter sounds. However, it is clear that the audiogram only provides limited insight into hearing loss, and fitting hearing aids based on this alone can lead to very diverse results in what is of most importance to patients, namely understanding speech. The difficulty of understanding speech in noise is one of the chief complaints of hearing aid users. The current project aims to improve personalized fitting of hearing aids to individual patients. The key technique will be the use of measurements taken directly from the brain's response to sound, by analysing the electroencephalographic (EEG) responses obtained from electrodes placed on the scalp. The analysis is 'objective', without requiring patients' voluntary and 'subjective' (and not always reliable) response to stimuli. We think this is important as it can be carried out in patients who are unable to provide such voluntary responses, for example infants or the elderly with dementia. By monitoring hearing without constant interruption to assess patients' perception, the performance of the hearing aid can also be assessed in natural listening conditions and over a longer time period. Ultimately this approach may also allow hearing aid settings to be adjusted without the presence of an audiologist, as users' needs and the auditory environment change. The test stimuli (hearing challenges) we will develop for the project will include a wider range of sounds than are currently routinely used in clinics, allowing for more subtle (differential) diagnosis of hearing loss, and a focus on the response to speech (including speech-in-noise).The key research aim in this project is to achieve a robust assessment of hearing function and speech processing in the brain (from the cochlea to the brain stem and cerebral cortex) by the computer analysis of EEG responses to complex real-world signals. This presents major scientific and technical challenges, needing the development of novel signal-analysis methods for speech and EEG data, which can be related to hearing impairment, cognition, as well as hearing aid settings and performance. The combination of these major challenges and a focus on patient benefit makes this an exciting and adventurous project. The main objectives of this proposal are to propose, assess and recommend:1. Signal processing methods to extract information from EEG signals on hearing performance and patients' access to speech2. Stimuli to use in assessing hearing3. Algorithms to optimize hearing aid fitting, based on parameters extracted from EEG responses This interdisciplinary work will be carried out as a collaboration between universities (hearing science, speech processing, signal analysis), industry (hearing technologies) and patients (choosing hearing challenges). The benefits of undertaking this work are expected to be to patients and their family and carers (improved quality of life from using hearing aids), the health-services (improved efficiency), industry (new diagnostic technologies) and the scientific community (better understanding of hearing; improved methods for analysing EEG signals).

据估计，英国约有600万人可以从助听器中受益，但只有约200万助听器用户，其中只有70%经常使用助听器。现代助听器是具有高级功能的复杂设备（不同频带的增益、振幅压缩、反馈消除、降噪、定向麦克风等）。需要专业人士来安装。助听器的益处有限是许多患者不经常使用助听器的主要原因。传统上，助听器主要基于“音频图”来安装，该音频图告知患者可以在不同频率下听到的最安静的声音（短音），并且从患者对逐渐安静的声音的自愿和主观反应（通常通过点击按钮）中获得。然而，很明显，听力图只能提供对听力损失的有限了解，仅基于此安装助听器可能会导致对患者最重要的结果，即理解语音。在噪声中理解语音的困难是助听器使用者的主要抱怨之一。目前的项目旨在改善助听器对个别患者的个性化选配。关键技术将是通过分析从头皮上的电极获得的脑电图（EEG）反应，直接从大脑对声音的反应中进行测量。分析是“客观的”，不需要患者对刺激的自愿和“主观”（并不总是可靠的）反应。我们认为这很重要，因为它可以在无法提供这种自愿反应的患者中进行，例如患有痴呆症的婴儿或老年人。通过在不间断的情况下监测听力来评估患者的感知，助听器的性能也可以在自然听力条件下和更长的时间内进行评估。最终，随着用户需求和听觉环境的变化，这种方法还可以在没有听力学家在场的情况下调整助听器设置。测试刺激（听力挑战）我们将开发的项目将包括更广泛的声音比目前常规使用的诊所，允许更微妙的听力损失的（鉴别）诊断，以及对言语反应的关注（包括噪声中的语音）。本项目的主要研究目的是对大脑的听觉功能和语音处理进行可靠的评估（从耳蜗到脑干和大脑皮层）通过计算机分析EEG对复杂现实世界信号的反应。这提出了重大的科学和技术挑战，需要开发新的语音和EEG数据信号分析方法，这些数据可能与听力障碍，认知以及助听器设置和性能有关。这些主要挑战和对患者利益的关注相结合，使这成为一个令人兴奋和冒险的项目。本提案的主要目的是提出、评估和建议：1.从EEG信号中提取关于听力表现和患者获得语音的信息的信号处理方法2。刺激用于评估听力3.基于从EEG响应中提取的参数优化助听器验配的算法这项跨学科工作将作为大学（听力科学，语音处理，信号分析），工业（听力技术）和患者（选择听力挑战）之间的合作进行。开展这项工作的好处预计将惠及患者及其家属和护理人员（使用助听器提高生活质量）、保健服务（提高效率）、工业（新的诊断技术）和科学界（更好地了解听力;改进分析脑电信号的方法）。

项目成果

Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs.

语音听觉的脑干反应：背景，刺激持续时间，辅音元音和时代数量的影响。

• DOI: 10.1097/aud.0000000000000648
• 发表时间: 2019
• 期刊: Ear and hearing
• 影响因子: 3.7
• 作者: BinKhamis G;Léger A;Bell SL;Prendergast G;O'Driscoll M;Kluk K
• 通讯作者: Kluk K

Supplemental material for Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures

成人助听器使用者言语听觉脑干反应的补充材料：辅助和背景噪声的影响以及行为测量的预测

• DOI: 10.25384/sage.8501234
• 发表时间: 2019
• 作者: BinKhamis G

Detecting cortical responses to continuous running speech using EEG data from only one channel.

仅使用来自一个通道的脑电图数据来检测皮层对连续运行语音的反应。

• DOI: 10.1080/14992027.2022.2035832
• 发表时间: 2023
• 期刊: International journal of audiology
• 影响因子: 2.7
• 作者: Aljarboa GS

Evoked responses to speech: Improving measurement, understanding limitations and exploring clinical applications

对言语的诱发反应：改进测量、了解局限性并探索临床应用

• 发表时间: 2020
• 作者: Bell S.l.

Can we measure Evoked Responses to speech?

我们可以测量对言语的诱发反应吗？

• 发表时间: 2017
• 作者: Bell, S.L.