With smartphone-based mobile electroencephalography (EEG), we can investigate sound perception beyond the lab. To understand sound perception in the real world, we need to relate naturally occurring sounds to EEG data. For this, EEG and audio information need to be synchronized precisely, only then it is possible to capture fast and transient evoked neural responses and relate them to individual sounds. We have developed Android applications (AFEx and Record-a) that allow for the concurrent acquisition of EEG data and audio features, i.e., sound onsets, average signal power (RMS), and power spectral density (PSD) on smartphone. In this paper, we evaluate these apps by computing event-related potentials (ERPs) evoked by everyday sounds. One participant listened to piano notes (played live by a pianist) and to a home-office soundscape. Timing tests showed a stable lag and a small jitter (< 3 ms) indicating a high temporal precision of the system. We calculated ERPs to sound onsets and observed the typical P1-N1-P2 complex of auditory processing. Furthermore, we show how to relate information on loudness (RMS) and spectra (PSD) to brain activity. In future studies, we can use this system to study sound processing in everyday life.

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With smartphone-based mobile electroencephalography (EEG), we can investigate sound perception beyond the lab. To understand sound perception in the real world, we need to relate naturally occurring sounds to EEG data. For this, EEG and audio information need to be synchronized precisely, only then it is possible to capture fast and transient evoked neural responses and relate them to individual sounds. We have developed Android applications (AFEx and Record-a) that allow for the concurrent acquisition of EEG data and audio features, i.e., sound onsets, average signal power (RMS) and power spectral density (PSD) on smartphone. In this paper, we evaluate these apps by computing event-related potentials (ERPs) evoked by everyday sounds. One participant listened to piano notes (played live by a pianist) and to a home-office soundscape. Timing tests showed that the temporal precision of the system is very good. We calculated ERPs to sound onsets and observed the typical P1-N1-P2 complex of auditory processing. Furthermore, we show how to relate information on loudness (RMS) and spectra (PSD) to brain activity. In future studies, we can use this system to study sound processing in everyday life.

Noise and reverberation affect speech intelligibility and increase listening effort. The impact is more severe for hearing-impaired listeners than for normal-hearing listeners and might lead to less social interactions, and reduced quality of life. While lab experiments are well controlled to get reliable outcomes, every-day listening situations are far more complex. Obtaining objective data of acoustical characteristics outside a laboratory is difficult, given the required equipment and its proper handling as well as privacy concerns emerging from audio recordings in a non-regulated and populated environment. Therefore, we developed a privacy-aware smartphone-based system that allows for long-term ecological momentary assessment. This system combines descriptions of the environment and subjective ratings on predefined scales with objective features derived from the acoustical signal. In a field study, forty-seven elderly listeners used the system for about four consecutive days. Results show that the listeners spend most of their time in environments with quite high signal-to-noise ratios resulting in high speech intelligibility ratings and low listening effort. More demanding situations are comparatively rare and include restaurant or car environments. The presentation summarizes the study results and discusses their contribution to further developments of today’s lab experiments towards more natural listening settings.