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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. To address this issue, we propose a parameter-free attention (PfA) mechanism that establishes a parameter-free linear space to bolster feature representation. The proposed PfA approach can be seamlessly integrated into the spiking neuron, resulting in enhanced performance without any increase in parameters. The experimental results on the SHD, BAE-TIDIGITS, SSC, DVS-Gesture, DVS-Cifar10, Cifar10, and Cifar100 datasets well demonstrate its competitive or superior classification accuracy compared with other state-of-the-art models. Furthermore, our model exhibits stronger noise robustness than conventional SNNs and those with parameterized attentional mechanisms. Our codes can be accessible at https://github.com/sunpengfei1122/PfA-SNN., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Ltd. All rights reserved.)

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. To address the cross-subject variability in EEG data, we also investigate persistent homology features that are robust to different types of noise. The performance of the different EEG representations is evaluated with the Support Vector Machine (SVM) accuracy on the WithMe data derived from a modified digit span experiment, and is benchmarked against baseline EEG-specific models, including a deep learning architecture known for effectively learning task-specific features., Results: The raw EEG time series outperform each of the considered data representations, but can fall short in comparison with the black-box deep learning approach that learns the best features., Discussion: The findings are limited to the WithMe experimental paradigm, highlighting the need for further studies on diverse tasks to provide a more comprehensive understanding of their utility in the analysis of EEG data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2025 Turkeš, Mortier, De Winne, Botteldooren, Devos, Latré and Verdonck.)

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. DLTC utilizes a learnable delay shift, which assigns varying levels of importance to different informational elements. This is complemented by an adjustable threshold that regulates firing times, allowing for earlier or later neuron activation as needed. We have tested DLTC's effectiveness in various contexts, including vision and auditory classification tasks, where it consistently outperformed traditional weight-only SNNs. The results indicate that DLTC achieves remarkable improvements in accuracy and computational efficiency, marking a step forward in advancing SNNs towards real-world applications. Our codes are accessible at https://github.com/sunpengfei1122/DLTC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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. This framework extracts patterns from audiovisual stimuli to model brain responses in EEG signals, while accounting for inherent response delays. By aligning audiovisual features with EEG signals through a shared embedding space, our approach improves the performance of brain-computer interface (BCI) systems. We also present WithMeAttention, a multimodal dataset designed to facilitate research in continuously distinguishing between target and distractor responses. Our methodology demonstrates a 3% improvement in accuracy on the WithMeAttention dataset compared to a baseline model that decodes EEG signals from scratch. This significant performance increase highlights the effectiveness of our approach Comprehensive analysis across four distinct conditions shows that rhythmic enhancement of visual information can optimize multi-sensory information processing. Notably, the two conditions featuring rhythmic target presentation - with and without accompanying beeps - achieved significantly superior performance compared to other scenarios. Furthermore, the delay distribution observed under different conditions indicates that our delay layer effectively emulates the neural processing delays in response to stimuli., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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. While an unfamiliar and chaotic soundscape can increase anxiety and stress, a well-designed soundscape can make the experience pleasant and improve moods. People with dementia suffer from a neurodegenerative disorder, leading to a progressive decline in cognitive health. Behavioural and psychological symptoms of dementia (BPSD) refer to a group of noncognitive behaviours that affect the prediction and control of dementia. Reducing the occurrence of BPSD is one of the main goals of dementia care. People with severe dementia usually live in nursing homes, long-term care facilities or memory care units where sensory perception is unfamiliar. The strange sensory stimuli add to the anxiety and distress of residents as care facilities are often not customized based on individual needs. Research shows that incorporating pleasant sounds into the environment, known as an 'augmented soundscape,' positively impacts behaviour and reduces BPSD. Most design tools and guidelines include eliminating unwanted noise. To date, there are no guidelines for sound augmentation in dementia care design. The previous study of sound selection showed promising results in sound augmentation and identifying sounds that a person with dementia may prefer during a specific time of the day. The sharpness and high-pitch sounds, such as animal localization, had positive results. Cricket sounds showed positive feedback during nighttime and evening; either its tranquillity made it favourable, or the continuous sound masked other noises during the night (like white noise). These results can be used to introduce sound augmentation guidelines. The goal is to introduce sound as a positive design implementation to enhance mood, reduce apathy and depression, lower anxiety and stress, and promote health.

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. The aim of this study was to assess the feasibility and impact of a soundscape intervention on people with dementia and behavioral symptoms., Research Design and Methods: Pilot single-blind repeated-measures randomized controlled trial of an augmented soundscape intervention. Participants were people with dementia in a hospital-based specialized dementia unit. Participants were randomized to an augmented soundscape intervention delivered in their room in the morning and evening or treatment as usual, with 2 baseline weeks and 4 weekly post-randomization assessments of the primary and secondary behavioral outcomes., Results: The soundscape intervention was feasible in terms of recruitment, retention, and delivery of the intervention. There were improvements in the neuropsychiatric inventory total scores over time in both groups (-5.89, 95%CI -8.45 to -3.28, p  < .001), but no differences between groups. There were no significant group, time, or group × time differences for the Pittsburgh Agitation Scale (PAS) total score. For the PAS-resisting care subscale, there was a significant group × time difference, with a greater reduction in the soundscape group over the study period (-0.81, 95% CI -1.59 to -0.03, p  = .042)., Discussion and Implications: In this pilot study, soundscape augmentation was a feasible and effective nonpharmacological approach to reducing resistance to care in people with dementia, although it did not improve neuropsychiatric symptoms more globally. Further studies with larger samples and of longer duration are needed to investigate the long-term effects of augmented sonic environments on people with dementia., Clinical Trials Registration Number: NCT04809545., Competing Interests: None., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America.)

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. Sessile marine invertebrates with a planktonic stage are particularly reliant on environmental cues to direct them to ideal habitats. As gregarious settlers, oysters prefer to settle amongst members of the same species. It has been hypothesized that oyster larvae from species Crassostrea virginica and Ostrea angasi use distinct conspecific oyster reef sounds to navigate to ideal habitats. In controlled laboratory experiments we exposed Pacific Oyster Magallana gigas larvae to anthropogenic sounds from conspecific oyster reefs, vessels, combined reef-vessel sounds as well as off-reef and no speaker controls. Our findings show that sounds recorded at conspecific reefs induced higher percentages of settlement by about 1.44 and 1.64 times compared to off-reef and no speaker controls, respectively. In contrast, the settlement increase compared to the no speaker control was non-significant for vessel sounds (1.21 fold), combined reef-vessel sounds (1.30 fold), and off-reef sounds (1.18 fold). This study serves as a foundational stepping stone for exploring larval sound feature preferences within this species., (© 2024. The Author(s).)

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. We achieved this objective by employing different machine learning (ML) methods for both individual-subject (IS) and cross-subject (CS) models. Finally, we investigated which EEG channels and time points were used by the model to make its predictions using saliency maps. We were able to successfully perform the aforementioned classification task for both the IS and CS scenarios, reaching classification accuracies up to 76%. In accordance with the literature, the model primarily used the parietal-occipital electrodes between 200 ms and 300 ms after the stimulus to make its prediction. The findings from this research contribute to the development of more effective P300-based brain-computer interfaces. Furthermore, they validate the EEG data collected in our experiment.

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. In this work, we demonstrate a learnable axonal delay combined with local skip-connections yields state-of-the-art performance on challenging benchmarks for spoken word recognition. Additionally, we introduce an auxiliary loss term to further enhance accuracy and stability. Experiments on the neuromorphic speech benchmark datasets, NTIDIDIGITS and SHD, show improvements in performance when incorporating our delay module in comparison to vanilla feedforward SNNs. Specifically, with the integration of our delay module, the performance on NTIDIDIGITS and SHD improves by 14% and 18%, respectively. When paired with local skip-connections and the auxiliary loss, our approach surpasses both recurrent and convolutional neural networks, yet uses 10 × fewer parameters for NTIDIDIGITS and 7 × fewer for SHD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Sun, Chua, Devos and Botteldooren.)

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. Thirty-five residents were included in the sample based on purposeful, homogeneous group characteristics and sampling. Empirical data were collected using 24/7 participatory observations. The collected data were analyzed using a phenomenological-hermeneutical method: a naïve understanding, a structural analysis and a comprehensive understanding. The result shows that the onset of CB depends on whether the resident feels safe and is triggered by an excess or lack of stimuli. The excess or shortage of stimuli and whether and when it affects a person is personal. It depends on various factors, the person's state and the time of day, the nature of the stimuli, familiarity, or strangeness is also a determining factor for the onset and progression of CB. The results can form an essential basis for developing soundscapes to make the PwD feel safe and reduce CB.

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. For this purpose a novel working memory paradigm was developed where participants are presented with a series of five target digits randomly interchanged with five distractor digits. Their goal is to remember the target digits and recall them orally. Depending on the condition support is provided by audio and/or rhythm. It is expected that the sound will lead to a better performance. It is also expected that this effect of sound is different in case of rhythmic and non-rhythmic sound. Last but not least, some variability is expected across participants. To make correct conclusions, the data of the experiment was statistically analyzed in a classic way, but also predictive models were developed in order to predict outcomes based on a range of input variables related to the experiment and the participant. The effect of auditory support could be confirmed, but no difference was observed between rhythmic and non-rhythmic sounds. Overall performance was indeed affected by individual differences, such as visual dominance or perceived task difficulty. Surprisingly a music education did not significantly affect the performance and even tended toward a negative effect. To better understand the underlying processes of attention, also brain activation data, e.g., by means of electroencephalography (EEG), should be recorded. This approach can be subject to a future work., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 De Winne, Devos, Leman and Botteldooren.)

Background: Traditional healthcare is centred around providing in-hospital services using hospital owned medical instruments. The COVID-19 pandemic has shown that this approach lacks flexibility to insure follow-up and treatment of common medical problems. In an alternative setting adapted to this problem, participatory healthcare can be considered centred around data provided by patients owning and operating medical data collection equipment in their homes., Objective: In order to trigger such a shift reliable and price attractive devices need to become available. Snoring, as a human sound production during sleep, can reflect sleeping behaviour and indicate sleep problems as an element of the overall health condition of a person., Methods: The use of off-the-shelf hardware from Internet of Things platforms and standard audio components allows the development of such devices. A prototype of a snoring sound detector with this purpose is developed., Results: The device, controlled by the patient and with specific snoring recording and analysing functions is demonstrated as a model for future participatory healthcare., Conclusions: Design of monitoring devices following this model could allow market introduction of new equipment for participatory healthcare, bringing a care complementary to traditional healthcare to the reach of patients, and could result in benefits from enhanced patient participation.

Sound and its resulting soundscape is a major appraisal component of the living environment. Where environmental sounds (e.g., outdoor traffic sounds) are often perceived as negative, a soundscape (e.g., containing natural sounds) can also have a positive effect on health and well-being. This supportive effect of a soundscape is getting increasing attention for use in practice. This paper addresses the design of a supportive sonic environment for persons with dementia in nursing homes. Starting from a review of key mechanisms related to sonic perception, cognitive deficits and related behavior, a framework is derived for the composition of a sonic environment for persons with dementia. The proposed framework is centered around using acoustic stimuli for influencing mood, stimulating the feeling of safety and triggering a response in a person. These stimuli are intended to be deployed as added sounds in a nursing home to improve the well-being and behavior of the residents.

Purpose: This paper describes an implementation of an Android application that allows cognitive functions to be tested., Methods: The application consists of a slightly modified Mini Mental state Examination (MMSE) test that provides examiner assistance in diagnosing cognitive impairments. The application deploys speech recognition techniques to allow easy and automated scoring of the test. The test results and test-related information are stored in a database providing easy access to data for follow-up and analysis, resulting in overall benefits to the examiner workflow. A small-scale pilot study of 5 months duration was conducted in a nursing home where 15 residents were tested with the MMSE app test and with the (paper) MMSE test with the aim of determining the agreement between the two test methods., Results: The final MMSE test scores, with a maximum score of 30, agree; the differences have a mean of 0.1, a standard deviation of 2.1 and fall in a [- 4, + 4] range as is illustrated in a Bland-Altman analysis. From examiner reflections, the motoric skills of the participant are indicated to contribute strongly to the time benefit of the assessment itself., Conclusions: The findings of this study suggest that the mobile digital version of the slightly modified MMSE test has the potential to be used as an attractive alternative for the conventional paper version of the test., (© 2019. European Geriatric Medicine Society.)

Pectinolytic Gram-negative bacteria were isolated from different waterways in the UK and Finland. Three strains (174/2(T), 181/2 and Dw054) had the same 16S rRNA gene sequences which shared 99% sequence similarity to species of the genus Dickeya, and a phylogeny of related genera confirmed attribution to this genus. Fatty acid profile analysis of all three strains found a high proportion of C16 : 1ω7c/C16 : 1ω7c and C16 : 0 fatty acids, and library profile searches found closest matches to Dickeya chrysanthemi. Production of a concatenated phylogeny using six loci, recA, gapA, atpD, gyrB, infB and rpoB, provided a high-resolution phylogeny which placed strains 174/2(T) and 181/2 as a distinct clade, separated from the other species of the genus Dickeya by a relatively long branch-length. DNA-DNA hybridization analysis with a limited number of reference species also supported the distinctiveness of strains 174/2(T) and 181/2 within the genus Dickeya. All three strains could be phenotypically distinguished from other species of the genus by fermentation of melibiose and raffinose but not D-arabinose or mannitol. The name Dickeya aquatica sp. nov. is proposed for the new taxon; the type strain is 174/2(T) ( = NCPPB 4580(T) = LMG 27354(T))., (© 2014 British Crown Copyright 2014.)

Blood flow velocities can be measured using a Doppler flow wire. This numerical study evaluates the impact of a 0.014" flow wire on the measured frequencies in a straight artery with diameters of 3 mm and 4 mm, under steady and pulsatile flow conditions. Simulations were performed with the wires positioned differently in the artery (perfectly centred and at an offset of 0.5 mm from the wall) and with different types of wire (tilted and straight). Measurements were taken at range gates from 4 mm to 10 mm. During simulations using a 3-mm vessel under pulsatile flow conditions, the relative error between the measured and reference maximum frequency (occurring in absence of the wire) decreased from 17.7% to 11.6% (with a mean value of 14.9%). During simulations using an off-centre 1.5 degree tilted wire, the mean error was approximately 5%. Therefore, our study suggests that that a centrally positioned flow wire is unfavourable for measuring flow velocities.

Because of its food spoiling capacity on the one hand and its significant role in the production of industrially valuable products on the other, Bacillus coagulans is of economic concern. Several studies have revealed a great deal of diversity within the species and this has led to a number of taxonomic adjustments. The present study aims to clarify the diversity within Bacillus coagulans sensu stricto and determine the taxonomic status of the species. Therefore, a polyphasic study was performed on a set of B. coagulans strains from diverse habitats. Techniques as ARDRA, SDS-PAGE of whole cell proteins, FAME analysis, routine phenotypic tests and rep-PCR illustrate considerable intra-species heterogeneity, while 16S rDNA sequence comparison and DNA-DNA relatedness support the accommodation of these strains in one species. Although most techniques demonstrate appreciable heterogeneity among the Bacillus coagulans strains, the intraspecies groupings are not consistent throughout all the methods applied and are not supported by any economic, historic or practical traits. Therefore, a division in subspecies seems inappropriate. In attempt to achieve a better species delineation, an emended description of Bacillus coagulans is included.