Who is WithMe? EEG features for attention in a visual task, with auditory and rhythmic support.

Introduction: The study of attention has been pivotal in advancing our comprehension of cognition. The goal of this study is to investigate which EEG data representations or features are most closely linked to attention, and to what extent they can handle the cross-subject variability.

Methods: We explore the features obtained from the univariate time series from a single EEG channel, such as time domain features and recurrence plots, as well as representations obtained directly from the multivariate time series, such as global field power or functional brain networks.

Towards parameter-free attentional spiking neural networks.

Brain-inspired spiking neural networks (SNNs) are increasingly explored for their potential in spatiotemporal information modeling and energy efficiency on emerging neuromorphic hardware. Recent works incorporate attentional modules into SNNs, greatly enhancing their capabilities in handling sequential data. However, these parameterized attentional modules have placed a huge burden on memory consumption, a factor that is constrained on neuromorphic chips.

Delayed knowledge transfer: Cross-modal knowledge transfer from delayed stimulus to EEG for continuous attention detection based on spike-represented EEG signals.

Decoding visual and auditory stimuli from brain activities, such as electroencephalography (EEG), offers promising advancements for enhancing machine-to-human interaction. However, effectively representing EEG signals remains a significant challenge. In this paper, we introduce a novel Delayed Knowledge Transfer (DKT) framework that employs spiking neurons for attention detection, using our experimental EEG dataset.

Soundscape Augmentation in Dementia Care Design; Need for a Guideline.

We navigate through the environment using our sensory stimuli. Sound is significant in guiding us through space and making us aware of time. Soundscape is an acoustic environment as perceived and experienced by a person.

Effect of Soundscape Augmentation on Behavioral Symptoms in People With Dementia: A Pilot Randomized Controlled Trial.

Background And Objectives: Sound is an important environmental factor that influences the expression of behavioral and psychological symptoms of dementia. Recent research on the effect of soundscape has shown promising results in improving environmental impact on people with dementia. However, no controlled studies have aimed to quantify the effects of soundscape intervention on resident outcomes.

Delay learning based on temporal coding in Spiking Neural Networks.

Spiking Neural Networks (SNNs) hold great potential for mimicking the brain's efficient processing of information. Although biological evidence suggests that precise spike timing is crucial for effective information encoding, contemporary SNN research mainly concentrates on adjusting connection weights. In this work, we introduce Delay Learning based on Temporal Coding (DLTC), an innovative approach that integrates delay learning with a temporal coding strategy to optimize spike timing in SNNs.

Comparison of the effects of reef and anthropogenic soundscapes on oyster larvae settlement.

Settlement is a critical period in the life cycle of marine invertebrates with a planktonic larval stage. For reef-building invertebrates such as oysters and corals, settlement rates are predictive for long-term reef survival. Increasing evidence suggests that marine invertebrates use information from ocean soundscapes to inform settlement decisions.

Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography.

Within the broader context of improving interactions between artificial intelligence and humans, the question has arisen regarding whether auditory and rhythmic support could increase attention for visual stimuli that do not stand out clearly from an information stream. To this end, we designed an experiment inspired by pip-and-pop but more appropriate for eliciting attention and P3a-event-related potentials (ERPs). In this study, the aim was to distinguish between targets and distractors based on the subject's electroencephalography (EEG) data.

Learnable axonal delay in spiking neural networks improves spoken word recognition.

Spiking neural networks (SNNs), which are composed of biologically plausible spiking neurons, and combined with bio-physically realistic auditory periphery models, offer a means to explore and understand human auditory processing-especially in tasks where precise timing is essential. However, because of the inherent temporal complexity in spike sequences, the performance of SNNs has remained less competitive compared to artificial neural networks (ANNs). To tackle this challenge, a fundamental research topic is the configuration of spike-timing and the exploration of more intricate architectures.

Home Monitoring of Asthma Exacerbations in Children and Adults With Use of an AI-Aided Stethoscope.

Purpose: The advent of new medical devices allows patients with asthma to self-monitor at home, providing a more complete picture of their disease than occasional in-person clinic visits. This raises a pertinent question: which devices and parameters perform best in exacerbation detection?

Methods: A total of 149 patients with asthma (90 children, 59 adults) participated in a 6-month observational study. Participants (or parents) regularly (daily for the first 2 weeks and weekly for the next 5.

Childhood sound disturbance and sleep problems in Alpine valleys with high levels of traffic exposures and greenspace.

Sound disturbance and sleep problems are regarded as the most common adverse effects of environmental noise but evidence of the role of air pollution and greenspace is scant. This is especially true for children who find themselves in a sensitive developmental period and experience their environment differently than adults. This study examined the joint effects of traffic exposures and residential greenspace on child sound disturbance and sleep problems via perceptions of neighborhood quality.

AI-based soundscape analysis: Jointly identifying sound sources and predicting annoyancea).

Soundscape studies typically attempt to capture the perception and understanding of sonic environments by surveying users. However, for long-term monitoring or assessing interventions, sound-signal-based approaches are required. To this end, most previous research focused on psycho-acoustic quantities or automatic sound recognition.

Time course of EEG complexity reflects attentional engagement during listening to speech in noise.

Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA).

Adopting a child perspective for exposome research on mental health and cognitive development - Conceptualisation and opportunities.

Mental disorders among children and adolescents pose a significant global challenge. The exposome framework covering the totality of internal, social and physical exposures over a lifetime provides opportunities to better understand the causes of and processes related to mental health, and cognitive functioning. The paper presents a conceptual framework on exposome, mental health, and cognitive development in children and adolescents, with potential mediating pathways, providing a possibility for interventions along the life course.

Advanced Noise Indicator Mapping Relying on a City Microphone Network.

In this work, a methodology is presented for city-wide road traffic noise indicator mapping. The need for direct access to traffic data is bypassed by relying on street categorization and a city microphone network. The starting point for the deterministic modeling is a previously developed but simplified dynamic traffic model, the latter necessary to predict statistical and dynamic noise indicators and to estimate the number of noise events.

Behavioural Models of Risk-Taking in Human-Robot Tactile Interactions.

Touch can have a strong effect on interactions between people, and as such, it is expected to be important to the interactions people have with robots. In an earlier work, we showed that the intensity of tactile interaction with a robot can change how much people are willing to take risks. This study further develops our understanding of the relationship between human risk-taking behaviour, the physiological responses by the user, and the intensity of the tactile interaction with a social robot.

Protective effect of restorative possibilities on cognitive function and mental health in children and adolescents: A scoping review including the role of physical activity.

Background The exposome approach can be a powerful tool for understanding the intertwining of social, physical, and internal influences that shape mental health and cognitive development throughout childhood. To distil conceptual models for subsequent analyses, the EU-funded project Early Environmental quality and Life-course mental health effects (Equal-Life) has conducted literature reviews on potential mediators linking the exposome to these outcomes. We report on a scoping review and a conceptual model of the role of restorative possibilities and physical activity.

The Influence of Everyday Acoustic Environments on the Challenging Behavior in Dementia: A Participatory Observation Study in Nursing Homes.

Challenging behavior (CB) is a group of behaviors, reactions and symptoms due to dementia, which can be challenging for the caregivers. The study aims to research the influence of acoustics on CB in people with dementia (PwD). An ethnographic method was used to study the daily life of PwD in their nursing homes with a specific focus on how people react to everyday environmental sounds.

With No Attention Specifically Directed to It, Rhythmic Sound Does Not Automatically Facilitate Visual Task Performance.

In a century where humans and machines-powered by artificial intelligence or not-increasingly work together, it is of interest to understand human processing of multi-sensory stimuli in relation to attention and working memory. This paper explores whether and when supporting visual information with rhythmic auditory stimuli can optimize multi-sensory information processing. In turn, this can make the interaction between humans or between machines and humans more engaging, rewarding and activating.

Early environmental quality and life-course mental health effects: The Equal-Life project.

Background: There is increasing evidence that a complex interplay of factors within environments in which children grows up, contributes to children's suboptimal mental health and cognitive development. The concept of the life-course exposome helps to study the impact of the physical and social environment, including social inequities, on cognitive development and mental health over time.

Methods: Equal-Life develops and tests combined exposures and their effects on children's mental health and cognitive development.

Parkinson's disease affects the neural alpha oscillations associated with speech-in-noise processing.

Parkinson's disease (PD) has increasingly been associated with auditory dysfunction, including alterations regarding the control of auditory information processing. Although these alterations may interfere with the processing of speech in degraded listening conditions, behavioural studies have generally found preserved speech-in-noise recognition in PD. However, behavioural speech audiometry does not capture the neurophysiological mechanisms supporting speech-in-noise processing.

Evaluation of multi-feature auditory deviance detection in Parkinson's disease: a mismatch negativity study.

Behavioral studies on auditory deviance detection in patients with Parkinson's disease (PD) have reported contradictory results. The primary aim of this study was to investigate auditory deviance detection of multiple auditory features in patients with PD by means of objective and reliable electroencephalographic (EEG) measurements. Twelve patients with early-stage PD and twelve age- and gender-matched healthy controls (HCs) were included in this study.

The Effect of Parkinson's Disease on Otoacoustic Emissions and Efferent Suppression of Transient Evoked Otoacoustic Emissions.

Purpose Several studies have demonstrated increased auditory thresholds in patients with Parkinson's disease (PD) based on subjective tonal audiometry. However, the pathophysiological mechanisms underlying auditory dysfunction in PD remain elusive. The primary aim of this study was to investigate cochlear and olivocochlear function in PD using objective measurements and to assess the effect of dopaminergic medication on auditory function.

Neurophysiological investigation of auditory intensity dependence in patients with Parkinson's disease.

There is accumulating evidence for auditory dysfunctions in patients with Parkinson's disease (PD). Moreover, a possible relationship has been suggested between altered auditory intensity processing and the hypophonic speech characteristics in PD. Nonetheless, further insight into the neurophysiological correlates of auditory intensity processing in patients with PD is needed primarily.

EEG Correlates of Learning From Speech Presented in Environmental Noise.

How the human brain retains relevant vocal information while suppressing irrelevant sounds is one of the ongoing challenges in cognitive neuroscience. Knowledge of the underlying mechanisms of this ability can be used to identify whether a person is distracted during listening to a target speech, especially in a learning context. This paper investigates the neural correlates of learning from the speech presented in a noisy environment using an ecologically valid learning context and electroencephalography (EEG).