2,953 research outputs found

    Design and Evaluation of a Hardware System for Online Signal Processing within Mobile Brain-Computer Interfaces

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    Brain-Computer Interfaces (BCIs) sind innovative Systeme, die eine direkte Kommunikation zwischen dem Gehirn und externen Geräten ermöglichen. Diese Schnittstellen haben sich zu einer transformativen Lösung nicht nur für Menschen mit neurologischen Verletzungen entwickelt, sondern auch für ein breiteres Spektrum von Menschen, das sowohl medizinische als auch nicht-medizinische Anwendungen umfasst. In der Vergangenheit hat die Herausforderung, dass neurologische Verletzungen nach einer anfänglichen Erholungsphase statisch bleiben, die Forscher dazu veranlasst, innovative Wege zu beschreiten. Seit den 1970er Jahren stehen BCIs an vorderster Front dieser Bemühungen. Mit den Fortschritten in der Forschung haben sich die BCI-Anwendungen erweitert und zeigen ein großes Potenzial für eine Vielzahl von Anwendungen, auch für weniger stark eingeschränkte (zum Beispiel im Kontext von Hörelektronik) sowie völlig gesunde Menschen (zum Beispiel in der Unterhaltungsindustrie). Die Zukunft der BCI-Forschung hängt jedoch auch von der Verfügbarkeit zuverlässiger BCI-Hardware ab, die den Einsatz in der realen Welt gewährleistet. Das im Rahmen dieser Arbeit konzipierte und implementierte CereBridge-System stellt einen bedeutenden Fortschritt in der Brain-Computer-Interface-Technologie dar, da es die gesamte Hardware zur Erfassung und Verarbeitung von EEG-Signalen in ein mobiles System integriert. Die Architektur der Verarbeitungshardware basiert auf einem FPGA mit einem ARM Cortex-M3 innerhalb eines heterogenen ICs, was Flexibilität und Effizienz bei der EEG-Signalverarbeitung gewährleistet. Der modulare Aufbau des Systems, bestehend aus drei einzelnen Boards, gewährleistet die Anpassbarkeit an unterschiedliche Anforderungen. Das komplette System wird an der Kopfhaut befestigt, kann autonom arbeiten, benötigt keine externe Interaktion und wiegt einschließlich der 16-Kanal-EEG-Sensoren nur ca. 56 g. Der Fokus liegt auf voller Mobilität. Das vorgeschlagene anpassbare Datenflusskonzept erleichtert die Untersuchung und nahtlose Integration von Algorithmen und erhöht die Flexibilität des Systems. Dies wird auch durch die Möglichkeit unterstrichen, verschiedene Algorithmen auf EEG-Daten anzuwenden, um unterschiedliche Anwendungsziele zu erreichen. High-Level Synthesis (HLS) wurde verwendet, um die Algorithmen auf das FPGA zu portieren, was den Algorithmenentwicklungsprozess beschleunigt und eine schnelle Implementierung von Algorithmusvarianten ermöglicht. Evaluierungen haben gezeigt, dass das CereBridge-System in der Lage ist, die gesamte Signalverarbeitungskette zu integrieren, die für verschiedene BCI-Anwendungen erforderlich ist. Darüber hinaus kann es mit einer Batterie von mehr als 31 Stunden Dauerbetrieb betrieben werden, was es zu einer praktikablen Lösung für mobile Langzeit-EEG-Aufzeichnungen und reale BCI-Studien macht. Im Vergleich zu bestehenden Forschungsplattformen bietet das CereBridge-System eine bisher unerreichte Leistungsfähigkeit und Ausstattung für ein mobiles BCI. Es erfüllt nicht nur die relevanten Anforderungen an ein mobiles BCI-System, sondern ebnet auch den Weg für eine schnelle Übertragung von Algorithmen aus dem Labor in reale Anwendungen. Im Wesentlichen liefert diese Arbeit einen umfassenden Entwurf für die Entwicklung und Implementierung eines hochmodernen mobilen EEG-basierten BCI-Systems und setzt damit einen neuen Standard für BCI-Hardware, die in der Praxis eingesetzt werden kann.Brain-Computer Interfaces (BCIs) are innovative systems that enable direct communication between the brain and external devices. These interfaces have emerged as a transformative solution not only for individuals with neurological injuries, but also for a broader range of individuals, encompassing both medical and non-medical applications. Historically, the challenge of neurological injury being static after an initial recovery phase has driven researchers to explore innovative avenues. Since the 1970s, BCIs have been at one forefront of these efforts. As research has progressed, BCI applications have expanded, showing potential in a wide range of applications, including those for less severely disabled (e.g. in the context of hearing aids) and completely healthy individuals (e.g. entertainment industry). However, the future of BCI research also depends on the availability of reliable BCI hardware to ensure real-world application. The CereBridge system designed and implemented in this work represents a significant leap forward in brain-computer interface technology by integrating all EEG signal acquisition and processing hardware into a mobile system. The processing hardware architecture is centered around an FPGA with an ARM Cortex-M3 within a heterogeneous IC, ensuring flexibility and efficiency in EEG signal processing. The modular design of the system, consisting of three individual boards, ensures adaptability to different requirements. With a focus on full mobility, the complete system is mounted on the scalp, can operate autonomously, requires no external interaction, and weighs approximately 56g, including 16 channel EEG sensors. The proposed customizable dataflow concept facilitates the exploration and seamless integration of algorithms, increasing the flexibility of the system. This is further underscored by the ability to apply different algorithms to recorded EEG data to meet different application goals. High-Level Synthesis (HLS) was used to port algorithms to the FPGA, accelerating the algorithm development process and facilitating rapid implementation of algorithm variants. Evaluations have shown that the CereBridge system is capable of integrating the complete signal processing chain required for various BCI applications. Furthermore, it can operate continuously for more than 31 hours with a 1800mAh battery, making it a viable solution for long-term mobile EEG recording and real-world BCI studies. Compared to existing research platforms, the CereBridge system offers unprecedented performance and features for a mobile BCI. It not only meets the relevant requirements for a mobile BCI system, but also paves the way for the rapid transition of algorithms from the laboratory to real-world applications. In essence, this work provides a comprehensive blueprint for the development and implementation of a state-of-the-art mobile EEG-based BCI system, setting a new benchmark in BCI hardware for real-world applicability

    Characterizing Sleep Patterns in Youth with CP and its Impact on Mood

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    Background. Cerebral palsy (CP) is a lifelong neurodevelopmental condition characterized by limitations in movement and posture (Oskoui et al., 2013; Rosenbaum et al., 2007). There is a growing consensus that sleep difficulties are common and life-long in individuals with CP (Lélis et al., 2016; Newman et al., 2006; Simard-Tremblay et al., 2011). These difficulties encompass various aspects such as sleep duration, sleep quality, staying asleep, and experiencing more difficulty getting up in the morning (Lélis et al., 2016; Newman et al., 2006); however, much remains unknown about the specific sleep patterns in CP and whether they are distinct from those observed in other conditions such as autism or fetal alcohol spectrum disorder (FASD). Additionally, the link between sleep and mood in CP is not well understood (Gadie et al., 2017). While in neurotypical youth, better sleep has been linked to improvements in social, emotional, and psychological well-being (e.g., mood), the extent to which sleep may impact mood within the context of CP remains uncertain (Hamilton et al., 2007). This manuscript-based thesis aims to address these significant gaps in knowledge by examining the sleep patterns in youth with CP and investigate the subsequent temporal association between sleep and mood. Methods. For this exploratory manuscript-based thesis, we analyzed secondary data from baseline questionnaires and weekly data (accelerometers and daily sleep diaries) collected from a larger study that examined the associations between physiological factors and mental health in youth with CP. In the first study, we investigated the sleep patterns of 45 youth with CP using caregiver and youth reports, the Child/Adolescent Sleep-Wake Scale (CSWS/ASWS), Insomnia Severity Index (ISI), and measurements from actigraphs that youth wore for one week. First, the sleep characteristics were described in relation to available demographic variables (e.g., sex, age, Gross Motor Functioning Classification System level [GMFCS]), using descriptive statistics. Second, to determine the impact of the presence of a mental health diagnosis on sleep patterns and problems, a hierarchical regression analyses was conducted. In the second study, we focused on a subsample of youth (n = 32) who had sufficient daily diaries of sleep and mood. In paper 2, the impact of intraindividual variability (IIV) in sleep patterns on mood (i.e., positive and negative affect) was examined using a series of fixed-effects multi-level modelling. Analyses included age, sex, and GMFCS as covariates as these factors contribute to sleep and mood. Results. In the first study of 45 youth, the average sleep duration was 10 hours per night (SD = 0:59), ranging from 7.5 to 12.85 hours. Youth experienced an average of 14 awakenings (>5 min) per night (SD = 5.3), which is substantially higher than previous literature in youth without CP. Most youth reported poor sleep quality based on sleep quality scores from the combined CSWS and ASWS (M = 3.67, SD = 1.24). Hierarchical linear regression analysis revealed a significant positive association between mental health diagnosis and insomnia severity, even after controlling for participant demographics (age, sex, GMFCS) (p = .010). For the second study, fixed-effect models were used to examine the association between IIV sleep duration and quality and next-day negative and positive affect over a 7-day period. While controlling for covariates, higher within-subjects variability of sleep quality was related to lower next-day negative mood (b = -.03, p < .001) and increased next-day positive mood (b = .05, p = .018). To determine the directionality of this association, mood variability predicting next day sleep was examined; however, only higher within-subject variability of negative mood was related to next-day sleep quality (b = -1.12, p = .011). Conclusions. This thesis is the first of its kind to examine the group and individual characteristics of sleep patterns among youth with CP (Study 1) and the temporal impact of IIV sleep on daily positive and negative affect (Study 2). Sleep is a complex phenomenon, and further investigation is necessary to understand the influence of various other factors, which were not available for this thesis. Nevertheless, sleep timing and sleep consistency may be important characteristics of sleep health. Overall, more research is needed to help inform prevention of mental health issues in this already vulnerable population and to help inform the development of supports for sleep

    Optical mapping and optogenetics in cardiac electrophysiology research and therapy:a state-of-the-art review

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    State-of-the-art innovations in optical cardiac electrophysiology are significantly enhancing cardiac research. A potential leap into patient care is now on the horizon. Optical mapping, using fluorescent probes and high-speed cameras, offers detailed insights into cardiac activity and arrhythmias by analysing electrical signals, calcium dynamics, and metabolism. Optogenetics utilizes light-sensitive ion channels and pumps to realize contactless, cell-selective cardiac actuation for modelling arrhythmia, restoring sinus rhythm, and probing complex cell–cell interactions. The merging of optogenetics and optical mapping techniques for ‘all-optical’ electrophysiology marks a significant step forward. This combination allows for the contactless actuation and sensing of cardiac electrophysiology, offering unprecedented spatial–temporal resolution and control. Recent studies have performed all-optical imaging ex vivo and achieved reliable optogenetic pacing in vivo, narrowing the gap for clinical use. Progress in optical electrophysiology continues at pace. Advances in motion tracking methods are removing the necessity of motion uncoupling, a key limitation of optical mapping. Innovations in optoelectronics, including miniaturized, biocompatible illumination and circuitry, are enabling the creation of implantable cardiac pacemakers and defibrillators with optoelectrical closed-loop systems. Computational modelling and machine learning are emerging as pivotal tools in enhancing optical techniques, offering new avenues for analysing complex data and optimizing therapeutic strategies. However, key challenges remain including opsin delivery, real-time data processing, longevity, and chronic effects of optoelectronic devices. This review provides a comprehensive overview of recent advances in optical mapping and optogenetics and outlines the promising future of optics in reshaping cardiac electrophysiology and therapeutic strategies

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    Differences in well-being:the biological and environmental causes, related phenotypes, and real-time assessment

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    Well-being is a complex, and multifaceted construct that includes feeling good and functioning well. There is a growing global recognition of well-being as an important research topic and public policy goal. Well-being is related to less behavioral and emotional problems, and is associated with many positive aspects of daily life, including longevity, higher educational achievement, happier marriage, and more productivity at work. People differ in their levels of well-being, i.e., some people are in general happier or more satisfied with their lives than others. These individual differences in well-being can arise from many different factors, including biological (genetic) influences and environmental influences. To enhance the development of future mental health prevention and intervention strategies to increase well-being, more knowledge about these determinants and factors underlying well-being is needed. In this dissertation, I aimed to increase the understanding of the etiology in a series of studies using different methods, including systematic reviews, meta-analyses, twin designs, and molecular genetic designs. In part I, we brought together all published studies on the neural and physiological factors underlying well-being. This overview allowed us to critically investigate the claims made about the biology involved in well-being. The number of studies on the neural and physiological factors underlying well-being is increasing and the results point towards potential correlates of well-being. However, samples are often still small, and studies focus mostly on a single biomarker. Therefore, more well-powered, data-driven, and integrative studies across biological categories are needed to better understand the neural and physiological pathways that play a role in well-being. In part II, we investigated the overlap between well-being and a range of other phenotypes to learn more about the etiology of well-being. We report a large overlap with phenotypes including optimism, resilience, and depressive symptoms. Furthermore, when removing the genetic overlap between well-being and depressive symptoms, we showed that well-being has unique genetic associations with a range of phenotypes, independently from depressive symptoms. These results can be helpful in designing more effective interventions to increase well-being, taking into account the overlap and possible causality with other phenotypes. In part III, we used the extreme environmental change during the COVID-19 pandemic to investigate individual differences in the effects of such environmental changes on well-being. On average, we found a negative effect of the pandemic on different aspects of well-being, especially further into the pandemic. Whereas most previous studies only looked at this average negative effect of the pandemic on well-being, we focused on the individual differences as well. We reported large individual differences in the effects of the pandemic on well-being in both chapters. This indicates that one-size-fits-all preventions or interventions to maintain or increase well-being during the pandemic or lockdowns will not be successful for the whole population. Further research is needed for the identification of protective factors and resilience mechanisms to prevent further inequality during extreme environmental situations. In part IV, we looked at the real-time assessment of well-being, investigating the feasibility and results of previous studies. The real-time assessment of well-being, related variables, and the environment can lead to new insights about well-being, i.e., results that we cannot capture with traditional survey research. The real-time assessment of well-being is therefore a promising area for future research to unravel the dynamic nature of well-being fluctuations and the interaction with the environment in daily life. Integrating all results in this dissertation confirmed that well-being is a complex human trait that is influenced by many interrelated and interacting factors. Future directions to understand individual differences in well-being will be a data-driven approach to investigate the complex interplay of neural, physiological, genetic, and environmental factors in well-being

    Methods to Improve Our Understanding of the Health and Welfare Status of Sheep (Ovis Aries) and the Influences of their Immediate Environment

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    Studies into the effective use of accelerometers in the automated assessment of sheep behaviour to improve welfare has increased exponentially with promising preliminary results. Previous research has focused primarily on explicit behaviour classification, for example, parturition and urination events, with a view to create a commercial tool that will provide health warnings for farmers. Yet the majority of trials have not been conducted in a farm environment. This study aims to provide essential primary research investigating environmental variables that may influence the behavioural patterns of a commercial flock. This vital information has been largely overlooked and crucial when considering tools that provide health warnings, due to the many factors that influence sheep behaviour such as weather, vegetation, soil type, land typography and breed (Hinch, 2017). The primary aim of this study was to assess the most appropriate model to predict the behaviours of commercial ewes. This was achieved by deploying accelerometers on a commercial flock and simultaneously collecting manual observations and video recordings of flock’s individual activity. The raw acceleration data was processed to create 6 variables. Behaviour classification was also evaluated using three ethograms, each with two mutually exclusive behavioural/postural states: 1. Head Position (head up/down), 2. Posture (standing/lying), 3. Activity (resting/grazing). Three Window setting (3, 5 and 7 seconds) and five machine learning algorithms 4 (Linear Discriminate Analysis (LDA), Classification and Regression Trees (CART), K Nearest Neighbour (KNN), Support Vector Machines (SVM) and Random Forest (RF)) were evaluated. Results indicated a RF with a 7 second window the optimal model across all ethograms. (Accuracy by ethogram; 1) 91.5%, 2) 91.0% and 3) 99.3%). The secondary aim of this study was to use a Linear Mixed Model (LMM) to investigate the influence of temperature and rainfall on grazing and resting behaviours. This was accomplished by using the initially developed model (RF) on data collected from an unsupervised commercial flock, recorded in a second trial. Results indicated that there was a significant positive relationship between grazing durations and rainfall (p.001), this finding conflicts with previous research observations and is yet unpublished. In addition, prior sheep behaviour research has suggested ‘foraging’ as the dominant activity, results from this trial indicate the dominant daily activity was resting (67% of daily activity). In conclusion this study highlights the difficultly of defining what ‘normal’ sheep behaviour is and that it is not viable to implement a ‘one-size fits all’ approach. Further research is required in the behavioural assessment for this particularly malleable species
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