Personalized fitting and evaluation of hearing aids with EEG responses

据估计，英国大约有600万人可以从助听器中受益，但助听器用户只有大约200万人，其中只有70%的人经常使用助听器。现代助听器是具有高级功能(不同频段的增益、幅度压缩、反馈消除、降噪、定向麦克风等)的复杂设备。并需要专业人士来安装它们。助听器带来的好处有限是许多患者不经常使用助听器的主要原因。按惯例，配戴助听器主要是根据“听力图”，即病人可以在不同的频率上听到最安静的声音(短音)，并根据病人对逐渐安静的声音的自愿和主观反应(通常通过点击按钮)来获得。然而，很明显，听力图只能提供有限的听力损失洞察力，仅基于这一点安装助听器就可以在对患者最重要的事情上产生非常不同的结果，即理解语言。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的个性化安装，以满足个别患者的需求。关键技术将是使用直接从大脑对声音的反应中获得的测量，通过分析放置在头皮上的电极获得的脑电(EEG)反应。分析是“客观的”，不要求患者对刺激做出自愿和“主观的”(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身上进行，例如婴儿或患有痴呆症的老年人。通过在没有持续中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段内进行评估。最终，随着用户的需求和听觉环境的改变，该方法还可以允许在没有听力专家在场的情况下调整助听器设置。我们将为该项目开发的测试刺激(听力挑战)将包括比目前临床常规使用的更广泛的声音，允许对听力损失进行更微妙(区分)的诊断，并将重点放在对语音(包括噪声中的语音)的响应上。该项目的关键研究目标是通过对复杂现实世界信号的脑电响应的计算机分析，实现对大脑(从耳蜗到脑干和大脑皮层)听力功能和语音处理的稳健评估。这带来了重大的科学和技术挑战，需要开发新的语音和脑电数据信号分析方法，这可能与听力障碍、认知以及助听器设置和性能有关。这些重大挑战和对患者利益的关注相结合，使这一项目变得令人兴奋和冒险。这项建议的主要目标是提出、评估和建议：1.从脑电信号中提取有关听力表现和患者获得言语的信息的信号处理方法2。用于评估听力的刺激物3。优化助听器配戴的算法，基于从脑电响应中提取的参数这项跨学科的工作将作为大学(听力科学、语音处理、信号分析)、行业(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是患者及其家人和护理人员(使用助听器改善生活质量)、卫生服务(提高效率)、行业(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)。

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).