Smartphone-based ear-electroencephalography to study sound processing in everyday life

Using mobile EEG to study auditory work strain during simulated surgical procedures

Surgical personnel face various stressors in the workplace, including environmental sounds. Mobile electroencephalography (EEG) offers a promising approach for objectively measuring how individuals perceive sounds. Because surgical performance does not necessarily decrease with higher levels of distraction, EEG could help guide noise reduction strategies that are independent of performance measures. In this study, we utilized mobile EEG to explore how a realistic soundscape is perceived during simulated laparoscopic surgery. To examine the varying demands placed on personnel in different situations, we manipulated the cognitive demand during the surgical task, using a memory task. To assess responses to the soundscape, we calculated event-related potentials for distinct sound events and temporal response functions for the ongoing soundscape. Although participants reported varying degrees of demand under different conditions, no significant effects were observed on surgical task performance or EEG parameters. However, changes in surgical task performance and EEG parameters over time were noted, while subjective results remained consistent over time. These findings highlight the importance of using multiple measures to fully understand the complex relationship between sound processing and cognitive demand. Furthermore, in the context of combined EEG and audio recordings in real-life scenarios, a sparse representation of the soundscape has the advantage that it can be recorded in a data-protected way compared to more detailed representations. However, it is unclear whether information get lost with sparse representations. Our results indicate that sparse and detailed representations are equally effective in eliciting neural responses. Overall, this study marks a significant step towards objectively investigating sound processing in applied settings.

High-density ear-EEG for understanding ear-centered EEG

Background. Mobile ear-EEG provides the opportunity to record EEG unobtrusively in everyday life. However, in real-life, the EEG data quickly becomes difficult to interpret, as the neural signal is contaminated by other, non-neural signal contributions. Due to the small number of electrodes in ear-EEG devices, the interpretation of the EEG becomes even more difficult. For meaningful and reliable ear-EEG, it is crucial that the brain signals we wish to record in real life are well-understood and that we make optimal use of the available electrodes. Their placement should be guided by prior knowledge about the characteristics of the signal of interest.Objective.We want to understand the signal we record with ear-EEG and make recommendations on how to optimally place a limited number of electrodes.Approach.We built a high-density ear-EEG with 31 channels spaced densely around one ear. We used it to record four auditory event-related potentials (ERPs): the mismatch negativity, the P300, the N100 and the N400. With this data, we gain an understanding of how different stages of auditory processing are reflected in ear-EEG. We investigate the electrode configurations that carry the most information and use a mass univariate ERP analysis to identify the optimal channel configuration. We additionally use a multivariate approach to investigate the added value of multi-channel recordings.Main results.We find significant condition differences for all ERPs. The different ERPs vary considerably in their spatial extent and different electrode positions are necessary to optimally capture each component. In the multivariate analysis, we find that the investigation of the ERPs benefits strongly from multi-channel ear-EEG.Significance.Our work emphasizes the importance of a strong theoretical and practical background when building and using ear-EEG. We provide recommendations on finding the optimal electrode positions. These results will guide future research employing ear-EEG in real-life scenarios.