Mechanism-based Approach to Optimization of Noise Reduction in Hearing Aids: Influence of Individual Traits on Outcomes and Preference

Despite tremendous advances in hearing aid technology, users still face difficulty understanding speech in noise. Most hearing aids make use of noise reduction algorithms to reduce background noise. However, such algorithms often distort speech sounds while reducing noise because the algorithm is unable to fully separate speech sounds and noise that occur at the same time, or in the same range of frequencies (pitches). People using hearing aids react very differently to the effects of noise reduction on speech sounds, but the individual traits that cause this variability are currently unknown. This variability in noise reduction outcomes and preference may arise from individual differences in tolerance to noise and speech distortions. These in turn are linked to individual variability in how the auditory system codes sound information as it passes from the inner ear to the brain, as well as differences in cognitive processes such as selective attention. While previous studies have tried to define these traits using behavioural, audiological, and self-report measures, the processes in the brain which underlie them are still not fully understood. In this project, the researchers will develop and evaluate measures of brain activity that could help us to understand individual tolerance to noise and speech cue distortions. The results of this study will form the basis for detailed follow-up investigations into the precise nature of sensorineural hearing loss and cognitive processes (e.g. cochlear processes that are impaired by hearing loss, cochlear synaptopathy (damage to the connections between the hair cells and their associated auditory nerve cells, which carry sound information to the brain), variability in working memory and attention) that lead to the observed outcomes. Most of the grant will be used to pay volunteers in the study for their time and travel and to pay for a research assistant to help collect the data. The researchers will study 25 people with hearing loss and 25 normal-hearing listeners whose behavioural and neural responses will be analysed. The results will provide pilot data for subsequent larger grant applications to test a large cohort of people with hearing loss to help to improve and develop more personalised hearing aid technologies. Listening outcomes in general, and the perceptual effects of noise reduction in particular, cannot be predicted from a person’s audiogram alone. Understanding how individual traits can affect outcomes and preference is crucial to the design of noise reduction algorithms and will allow clinicians to select the best noise reduction algorithm/setting to use for each listener. By understanding and defining the processes in the brain which underlie tolerance to noise and speech distortions, the researchers will provide new insights into noise reduction outcomes and preferences and provide the groundwork to improve noise reduction programmes in hearing aids so that they can benefit more people.