Hearing aid (HA) users often experience increased listening effort, particularly in noisy environments. While noise reduction (NR) algorithms aim to alleviate this, traditional electroencephalography (EEG) methods based on power analysis have limited success in assessing the listening effort in this population. This study proposes a novel method using a whole-head synchronization map analysis that uses local connectivity, a measure of statistical dependencies within localized brain regions. We use EEG electrodes to define a region based on the surrounding electrodes in the first-order neighborhood. This approach was tested using EEG data from 22 HA users with active or inactive NR engaged in a continuous speech-in-noise (SiN) task at low (3dB) and high (8dB) signal-to-noise ratio (SNR) levels. Whole-head synchronization was quantified using circular omega complexity (COC), a multivariate phase synchrony measure. Results showed increased local connectivity in the alpha band (8–12 Hz) within frontal and occipital regions during SiN condition compared to the background noise-only (NO) condition. Furthermore, NR activation impacted the synchronization map differently at the two SNRs of the experiment, with greater effect observed at low SNR, primarily in the left parietal region and alpha band. This behavior is in line with that of existing measures for listening effort, and therefore suggests that EEG local connectivity analysis holds promise as a tool for objectively assessing listening effort in HA users, especially in challenging listening environments.

In this study, we investigate integrating eye tracking with auditory attention decoding (AAD) using portable EEG devices, specifically a mobile EEG cap and cEEGrid, in a preliminary analysis with a single participant. A novel audiovisual dataset was collected using a mobile EEG system designed to simulate real-life listening environments. Our study has two main objectives: (1) to use eye tracking data to automatically infer the labels of attended and unattended speech streams, and (2) to train an AAD model using these estimated labels, evaluating its performance through speech reconstruction accuracy. The results demonstrate the feasibility of using eye tracking data to estimate attended speech labels, which were then used to train speech reconstruction models. We validated our models with varying amounts of training data and a second dataset from the same participant to assess generalization. Additionally, we examined the impact of mislabeling on AAD accuracy. These findings provide preliminary evidence that eye tracking can be used to infer speech labels, offering a potential pathway for brain-controlled hearing aids, where true labels are unknown.