4,014 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

    A Spark Of Emotion: The Impact of Electrical Facial Muscle Activation on Emotional State and Affective Processing

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    Facial feedback, which involves the brain receiving information about the activation of facial muscles, has the potential to influence our emotional states and judgments. The extent to which this applies is still a matter of debate, particularly considering a failed replication of a seminal study. One factor contributing to the lack of replication in facial feedback effects may be the imprecise manipulation of facial muscle activity in terms of both degree and timing. To overcome these limitations, this thesis proposes a non-invasive method for inducing precise facial muscle contractions, called facial neuromuscular electrical stimulation (fNMES). I begin by presenting a systematic literature review that lays the groundwork for standardising the use of fNMES in psychological research, by evaluating its application in existing studies. This review highlights two issues, the lack of use of fNMES in psychology research and the lack of parameter reporting. I provide practical recommendations for researchers interested in implementing fNMES. Subsequently, I conducted an online experiment to investigate participants' willingness to participate in fNMES research. This experiment revealed that concerns over potential burns and involuntary muscle movements are significant deterrents to participation. Understanding these anxieties is critical for participant management and expectation setting. Subsequently, two laboratory studies are presented that investigated the facial FFH using fNMES. The first study showed that feelings of happiness and sadness, and changes in peripheral physiology, can be induced by stimulating corresponding facial muscles with 5–seconds of fNMES. The second experiment showed that fNMES-induced smiling alters the perception of ambiguous facial emotions, creating a bias towards happiness, and alters neural correlates of face processing, as measured with event-related potentials (ERPs). In summary, the thesis presents promising results for testing the facial feedback hypothesis with fNMES and provides practical guidelines and recommendations for researchers interested in using fNMES for psychological research

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Sound Event Detection by Exploring Audio Sequence Modelling

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    Everyday sounds in real-world environments are a powerful source of information by which humans can interact with their environments. Humans can infer what is happening around them by listening to everyday sounds. At the same time, it is a challenging task for a computer algorithm in a smart device to automatically recognise, understand, and interpret everyday sounds. Sound event detection (SED) is the process of transcribing an audio recording into sound event tags with onset and offset time values. This involves classification and segmentation of sound events in the given audio recording. SED has numerous applications in everyday life which include security and surveillance, automation, healthcare monitoring, multimedia information retrieval, and assisted living technologies. SED is to everyday sounds what automatic speech recognition (ASR) is to speech and automatic music transcription (AMT) is to music. The fundamental questions in designing a sound recognition system are, which portion of a sound event should the system analyse, and what proportion of a sound event should the system process in order to claim a confident detection of that particular sound event. While the classification of sound events has improved a lot in recent years, it is considered that the temporal-segmentation of sound events has not improved in the same extent. The aim of this thesis is to propose and develop methods to improve the segmentation and classification of everyday sound events in SED models. In particular, this thesis explores the segmentation of sound events by investigating audio sequence encoding-based and audio sequence modelling-based methods, in an effort to improve the overall sound event detection performance. In the first phase of this thesis, efforts are put towards improving sound event detection by explicitly conditioning the audio sequence representations of an SED model using sound activity detection (SAD) and onset detection. To achieve this, we propose multi-task learning-based SED models in which SAD and onset detection are used as auxiliary tasks for the SED task. The next part of this thesis explores self-attention-based audio sequence modelling, which aggregates audio representations based on temporal relations within and between sound events, scored on the basis of the similarity of sound event portions in audio event sequences. We propose SED models that include memory-controlled, adaptive, dynamic, and source separation-induced self-attention variants, with the aim to improve overall sound recognition

    Unravelling the complex reproductive tactics of male humpback whales : an integrative analysis of paternity, age, testosterone, and genetic diversity

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    How the underlying forces of sexual selection impact reproductive tactics including elaborate acoustic displays in cetaceans remains poorly understood. Here, I combined 26 years (1995-2020) of photo-identification, behavioural, (epi)genetic, and endocrine data from an endangered population of humpback whales (New Caledonia), to explore male reproductive success, age, physiology, and population dynamics over almost a third of the lifespan of a humpback whale. First, I conducted a paternity analysis on 177 known mother-offspring pairs and confirmed previous findings of low variation in reproductive success in male humpback whales. Second, epigenetic age estimates of 485 males revealed a left-skewed population age structure in the first half of the study period that became more balanced in the second half. Further, older males (> 23 years) more often engaged in certain reproductive tactics (singing and escorting) and were more successful in siring offspring once the population age structure stabilised, suggesting reproductive tactics and reproductive success in male humpback whales may be age-dependent. Third, using enzyme immunoassays on 457 blubber samples, I observed a seasonal decline in male testosterone in the population over the breeding season. Testosterone levels appeared highest during puberty, then decreased and levelled off at the onset of maturity, yet were highly variable at any point during the breeding season and across males of all ages. Lastly, I investigated the influence of genetic diversity at the major histocompatibility complex (MHC) class I and class IIa (DQB and DRB-a) on patterns of male reproductive success in humpback whales. Mating pairs shared fewer alleles than expected under random mating at MHC class I and IIa, thus, providing evidence of an MHC-mediated female mate choice in humpback whales. This thesis provides novel, critical insights into the evolutionary consequences of commercial whaling on the demography, patterns of reproduction and sexual selection of exploited populations of baleen whales."This work was supported by a University of St Andrews School of Biology Ph.D. Scholarship and the Louis M. Herman Research Scholarship 2022 to Franca Eichenberger. Sample collection and analyses from 2018-2020 were supported by grants to Ellen C. Garland (Royal Society University Research Fellowship (UF160081 & URF\R\221020), Royal Society Research Fellows Enhancement Award (RGF\EA\180213), Royal Society Research Grants for Research Fellows 2018 (RGF\R1\181014), National Geographic Grant (#NGS-50654R-18), Carnegie Trust Research Incentive Grant (RIG007772), British Ecological Society Small Research Grant (SR18/1288) and School of Biology Research Committee funding)."--Fundin

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Machine learning applications in search algorithms for gravitational waves from compact binary mergers

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    Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe. However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing. In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software. Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals

    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
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