Improving Tracking of Selective Attention in Hearing Aid Users: The Role of Noise Reduction and Nonlinearity Compensation.

Hearing impairment (HI) disrupts social interaction by hindering the ability to follow conversations in noisy environments. While hearing aids (HAs) with noise reduction (NR) partially address this, the "cocktailparty problem" persists, where individuals struggle to attend to specific voices amidst background noise. This study investigated how NR and an advanced signal processing method for compensating for nonlinearities in EEG signals can improve neural speech processing in HI listeners. Participants wore hearing aids with NR, either activated or deactivated, while focusing on target speech amidst competing masker speech and background noise. Analysis focused on temporal response functions to assess neural tracking of relevant target and masker speech. Results revealed enhanced neural responses (N1 and P2) to target speech, particularly in frontal and central scalp regions, when NR was activated. Additionally, a novel method compensated for nonlinearities in EEG data, leading to improved signal-to-noise ratio (SNR) and potentially revealing more precise neural tracking of relevant speech. This effect was most prominent in the left-frontal scalp region. Importantly, NR activation significantly improved the effectiveness of this method, leading to stronger responses and reduced variance in EEG data and potentially revealing more precise neural tracking of relevant speech. This study provides valuable insights into the neural mechanisms underlying NR benefits and introduces a promising EEG analysis approach sensitive to NR effects, paving the way for potential improvements in HAs. Understanding how hearing aids (HAs) with noise reduction (NR) improve selective auditory attention in noisy environments is crucial for future advancements. This study investigated the neural effects of NR in hearing-impaired listeners using EEG. We observed significantly enhanced neural responses (N1 and P2 peaks) to target speech with NR activated, suggesting improved speech tracking in frontal and central scalp regions. The advanced signal processing method also compensated for nonlinearities in EEG data, improving the signal-to-noise ratio (SNR) and revealing more precise neural tracking, particularly in the left-frontal scalp region. This study sheds light on the neural mechanisms behind NR benefits and introduces a promising EEG analysis method sensitive to NR effects, paving the way for optimizing future HAs.