Exploring Relevant Features for EEG-Based Investigation of Sound Perception in Naturalistic Soundscapes.

A comprehensive analysis of everyday sound perception can be achieved using electroencephalography (EEG) with the concurrent acquisition of information about the environment. While extensive research has been dedicated to speech perception, the complexities of auditory perception within everyday environments, specifically the types of information and the key features to extract, remain less explored. Our study aims to systematically investigate the relevance of different feature categories: discrete sound-identity markers, general cognitive state information, and acoustic representations, including discrete sound onset, the envelope, and mel-spectrogram.

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.

Investigating the attentional focus to workplace-related soundscapes in a complex audio-visual-motor task using EEG.

Introduction: In demanding work situations (e.g., during a surgery), the processing of complex soundscapes varies over time and can be a burden for medical personnel.

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

. 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.

Assessing focus through ear-EEG: a comparative study between conventional cap EEG and mobile in- and around-the-ear EEG systems.

Introduction: As our attention is becoming a commodity that an ever-increasing number of applications are competing for, investing in modern day tools and devices that can detect our mental states and protect them from outside interruptions holds great value. Mental fatigue and distractions are impacting our ability to focus and can cause workplace injuries. Electroencephalography (EEG) may reflect concentration, and if EEG equipment became wearable and inconspicuous, innovative brain-computer interfaces (BCI) could be developed to monitor mental load in daily life situations.

A Systematic Comparison of High-End and Low-Cost EEG Amplifiers for Concealed, Around-the-Ear EEG Recordings.

Wearable electroencephalography (EEG) has the potential to improve everyday life through brain-computer interfaces (BCI) for applications such as sleep improvement, adaptive hearing aids, or thought-based digital device control. To make these innovations more practical for everyday use, researchers are looking to miniaturized, concealed EEG systems that can still collect neural activity precisely. For example, researchers are using flexible EEG electrode arrays that can be attached around the ear (cEEGrids) to study neural activations in everyday life situations.

Recording Brain Activity with Ear-Electroencephalography.

The c-grid (ear-electroencephalography, sold under the name cEEGrid) is an unobtrusive and comfortable electrode array that can be used for investigating brain activity after affixing around the ear. The c-grid is suitable for use outside of the laboratory for long durations, even for the whole day. Various cognitive processes can be studied using these grids, as shown by previous research, including research beyond the lab.

Opportunities and Limitations of Mobile Neuroimaging Technologies in Educational Neuroscience.

As the field of educational neuroscience continues to grow, questions have emerged regarding the ecological validity and applicability of this research to educational practice. Recent advances in mobile neuroimaging technologies have made it possible to conduct neuroscientific studies directly in naturalistic learning environments. We propose that embedding mobile neuroimaging research in a cycle (Matusz, Dikker, Huth, & Perrodin, 2019), involving lab-based, seminaturalistic, and fully naturalistic experiments, is well suited for addressing educational questions.

Ear-EEG compares well to cap-EEG in recording auditory ERPs: a quantification of signal loss.

. Ear-EEG (electroencephalography) allows to record brain activity using only a few electrodes located close to the ear. Ear-EEG is comfortable and easy to apply, facilitating beyond-the-lab EEG recordings in everyday life.

Real-Time Audio Processing of Real-Life Soundscapes for EEG Analysis: ERPs Based on Natural Sound Onsets.

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.

Pocketable Labs for Everyone: Synchronized Multi-Sensor Data Streaming and Recording on Smartphones with the Lab Streaming Layer.

The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly available for stationary setups.

Flex-Printed Ear-EEG Sensors for Adequate Sleep Staging at Home.

A comfortable, discrete and robust recording of the sleep EEG signal at home is a desirable goal but has been difficult to achieve. We investigate how well flex-printed electrodes are suitable for sleep monitoring tasks in a smartphone-based home environment. The cEEGrid ear-EEG sensor has already been tested in the laboratory for measuring night sleep.

Behavioral and Neural Dynamics of Interpersonal Synchrony Between Performing Musicians: A Wireless EEG Hyperscanning Study.

Interpersonal synchrony refers to the temporal coordination of actions between individuals and is a common feature of social behaviors, from team sport to ensemble music performance. Interpersonal synchrony of many rhythmic (periodic) behaviors displays dynamics of coupled biological oscillators. The current study addresses oscillatory dynamics on the levels of brain and behavior between music duet partners performing at spontaneous (uncued) rates.

The Sensitivity of Ear-EEG: Evaluating the Source-Sensor Relationship Using Forward Modeling.

Ear-EEG allows to record brain activity in every-day life, for example to study natural behaviour or unhindered social interactions. Compared to conventional scalp-EEG, ear-EEG uses fewer electrodes and covers only a small part of the head. Consequently, ear-EEG will be less sensitive to some cortical sources.

Decoding the Attended Speaker From EEG Using Adaptive Evaluation Intervals Captures Fluctuations in Attentional Listening.

Listeners differ in their ability to attend to a speech stream in the presence of a competing sound. Differences in speech intelligibility in noise cannot be fully explained by the hearing ability which suggests the involvement of additional cognitive factors. A better understanding of the temporal fluctuations in the ability to pay selective auditory attention to a desired speech stream may help in explaining these variabilities.

An approach to social flexibility: Congruency effects during spontaneous word-by-word interaction.

Cognitive flexibility is the ability to switch between different concepts or to adapt goal-directed behavior in a changing environment. Although, cognitive research on this ability has long been focused on the individual mind, it is becoming increasingly clear that cognitive flexibility plays a central role in our social life. This is particularly evident in turn-taking in verbal conversation, where cognitive flexibility of the individual becomes part of social flexibility in the dyadic interaction.

Expectancy effects in the EEG during joint and spontaneous word-by-word sentence production in German.

Our aim in the present study is to measure neural correlates during spontaneous interactive sentence production. We present a novel approach using the word-by-word technique from improvisational theatre, in which two speakers jointly produce one sentence. This paradigm allows the assessment of behavioural aspects, such as turn-times, and electrophysiological responses, such as event-related-potentials (ERPs).

Studying brain activity during word-by-word interactions using wireless EEG.

We introduce here the word-by-word paradigm, a dynamic setting, in which two people take turns in producing a single sentence. This task requires a high degree of coordination between the partners and the simplicity of the task allows us to study with sufficient experimental control behavioral and neural processes that underlie this controlled interaction. For this study, 13 pairs of individuals engaged in a scripted word-by-word interaction, while we recorded the neural activity of both participants simultaneously using wireless EEG.

Decoding Selective Attention in Normal Hearing Listeners and Bilateral Cochlear Implant Users With Concealed Ear EEG.

Electroencephalography (EEG) data can be used to decode an attended speech source in normal-hearing (NH) listeners using high-density EEG caps, as well as around-the-ear EEG devices. The technology may find application in identifying the target speaker in a cocktail party like scenario and steer speech enhancement algorithms in cochlear implants (CIs). However, the worse spectral resolution and the electrical artifacts introduced by a CI may limit the applicability of this approach to CI users.

A Riemannian Modification of Artifact Subspace Reconstruction for EEG Artifact Handling.

Artifact Subspace Reconstruction (ASR) is an adaptive method for the online or offline correction of artifacts comprising multichannel electroencephalography (EEG) recordings. It repeatedly computes a principal component analysis (PCA) on covariance matrices to detect artifacts based on their statistical properties in the component subspace. We adapted the existing ASR implementation by using Riemannian geometry for covariance matrix processing.

Epidural recordings in cochlear implant users.

Objective: In the long term it is desirable for CI users to control their device via brain signals. A possible strategy is the use of auditory evoked potentials (AEPs). Several studies have shown the suitability of auditory paradigms for such an approach.