1,547 research outputs found

    EEG-based neurophysiological indices for expert psychomotor performance – a review

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    A primary objective of current human neuropsychological performance research is to define the physiological correlates of adaptive knowledge utilization, in order to support the enhanced execution of both simple and complex tasks. Within the present article, electroencephalography-based neurophysiological indices characterizing expert psychomotor performance, will be explored. As a means of characterizing fundamental processes underlying efficient psychometric performance, the neural efficiency model will be evaluated in terms of alpha-wave-based selective cortical processes. Cognitive and motor domains will initially be explored independently, which will act to encapsulate the task-related neuronal adaptive requirements for enhanced psychomotor performance associating with the neural efficiency model. Moderating variables impacting the practical application of such neuropsychological model, will also be investigated. As a result, the aim of this review is to provide insight into detectable task-related modulation involved in developed neurocognitive strategies which support heightened psychomotor performance, for the implementation within practical settings requiring a high degree of expert performance (such as sports or military operational settings)

    Human movement modifications induced by different levels of transparency of an active upper limb exoskeleton

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    Active upper limb exoskeletons are a potentially powerful tool for neuromotor rehabilitation. This potential depends on several basic control modes, one of them being transparency. In this control mode, the exoskeleton must follow the human movement without altering it, which theoretically implies null interaction efforts. Reaching high, albeit imperfect, levels of transparency requires both an adequate control method and an in-depth evaluation of the impacts of the exoskeleton on human movement. The present paper introduces such an evaluation for three different “transparent” controllers either based on an identification of the dynamics of the exoskeleton, or on force feedback control or on their combination. Therefore, these controllers are likely to induce clearly different levels of transparency by design. The conducted investigations could allow to better understand how humans adapt to transparent controllers, which are necessarily imperfect. A group of fourteen participants were subjected to these three controllers while performing reaching movements in a parasagittal plane. The subsequent analyses were conducted in terms of interaction efforts, kinematics, electromyographic signals and ergonomic feedback questionnaires. Results showed that, when subjected to less performing transparent controllers, participants strategies tended to induce relatively high interaction efforts, with higher muscle activity, which resulted in a small sensitivity of kinematic metrics. In other words, very different residual interaction efforts do not necessarily induce very different movement kinematics. Such a behavior could be explained by a natural human tendency to expend effort to preserve their preferred kinematics, which should be taken into account in future transparent controllers evaluation

    Open or Closed? Measurement Performance of Open- and Closed-Path Methane Sensors for Mobile Emissions Screening

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    Ground-based vehicle systems are being increasingly used by industry, regulators, and service providers in the upstream oil and gas sector to measure methane emissions. However, the suite of methane sensors affixed to these systems is non-standardized and existing literature displays a scarcity of direct comparisons regarding their measurement performance. Gaussian dispersion models are often used to supplement measured data and derive estimates of emission intensity in screening applications based on data measured by these sensors. Existing literature indicates these models perform with considerable uncertainty. As such, equivalence of performance between existing vehicle-based emission screening systems is difficult to assess. To address this issue, field-based controlled release experiments were conducted to compare concentration data from an open- and closed-path sensor deployed in tandem onboard a vehicle. Performance of a forward Gaussian dispersion model was assessed relative to measured data from both sensors. 801 transects were driven through methane plumes dispersed downwind of a controlled emission source at various measurement distances and driving speeds, as well as a range of atmospheric conditions. Measurement performance was predicated on three primary descriptors of concentration data: the maximum concentration within each plume (maximum enhancement), plume width, and plume area (total methane sampled within the plume). Results showed that the measurement performances of both sensors were not equivalent. Relative to the open-path sensor, the closed-path sensor reported maximum enhancements that were ~40% smaller on average and plume widths that were ~42% larger on average, while measures of plume area displayed near 1:1 parity. Measurement discrepancies are largely explained by differences in sensor measurement frequency and intrinsic sampling mechanisms. Forward Gaussian dispersion model performance displayed uncertainties ranging from 12.3% to 1207.0%. The origin of this uncertainty is largely determined by generalizations of atmospheric stability and simplistic representations of downwind plume migration within the model

    Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses Using Deep Neural Networks

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    This research compares classification accuracy obtained with the classical classification techniques and the presented convolutional neural network for the recognition of hand gestures used in robotic prostheses for transradial amputees using surface electromyography (sEMG) signals. The first two classifiers are the most used in the literature: support vector machines (SVM) and artificial neural networks (ANN). A new convolutional neural network (CNN) architecture based on the AtzoriNet network is proposed to assess performance according to amputation-related variables. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods and The performance it is compared with other CNN proposed by other authors. The performance of the CNN is evaluated with different metrics, providing good results compared to those proposed by other authors in the literature

    Investigating Lyman continuum escape fractions of high redshift galaxies during the era of reionization

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    Peering into the Dark: Investigating dark matter and neutrinos with cosmology and astrophysics

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    The LCDM model of modern cosmology provides a highly accurate description of our universe. However, it relies on two mysterious components, dark matter and dark energy. The cold dark matter paradigm does not provide a satisfying description of its particle nature, nor any link to the Standard Model of particle physics. I investigate the consequences for cosmological structure formation in models with a coupling between dark matter and Standard Model neutrinos, as well as probes of primordial black holes as dark matter. I examine the impact that such an interaction would have through both linear perturbation theory and nonlinear N-body simulations. I present limits on the possible interaction strength from cosmic microwave background, large scale structure, and galaxy population data, as well as forecasts on the future sensitivity. I provide an analysis of what is necessary to distinguish the cosmological impact of interacting dark matter from similar effects. Intensity mapping of the 21 cm line of neutral hydrogen at high redshift using next generation observatories, such as the SKA, would provide the strongest constraints yet on such interactions, and may be able to distinguish between different scenarios causing suppressed small scale structure. I also present a novel type of probe of structure formation, using the cosmological gravitational wave signal of high redshift compact binary mergers to provide information about structure formation, and thus the behaviour of dark matter. Such observations would also provide competitive constraints. Finally, I investigate primordial black holes as an alternative dark matter candidate, presenting an analysis and framework for the evolution of extended mass populations over cosmological time and computing the present day gamma ray signal, as well as the allowed local evaporation rate. This is used to set constraints on the allowed population of low mass primordial black holes, and the likelihood of witnessing an evaporation

    Life in the Fells: names in a nineteenth-century Cumberland landscape

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    This thesis examines the field-names of Crosthwaite parish, Cumberland. A survey of the fieldnames and a corresponding glossary of elements and their localised usage(s) within the study area, some previously unattested, form a significant part of the thesis. The field-name data is compiled chiefly from nineteenth-century Tithe Awards which records the names and descriptions of Crosthwaite’s 8,626 land units, 3,351 of which are field-names (3.4.1). These 3,351 field-names, recorded in the survey (Chapter Four), contain 6,052 elements which fall into 586 element types, presented in the glossary (Chapter Five). The work of this thesis is underpinned by the data from two key resources which were created as part of this research: a) a field-name dataset composed of all linguistic data held within the Tithe Awards for the parish (3.1); and b) an interactive digital map of all 8,626 land units, into which the field-name data is embedded (3.3). The first resource – the onomastic data – allows for the fieldnames to be analysed linguistically. The second – the cartographical data – allows for the fieldnames to be analysed spatially, enabling the evidence of the landscape to inform the interpretation and analysis of the names. A quantitative analysis of all Crosthwaite’s field-name elements (Chapter Six) highlights the close relationship between the language of the field-names and the landscape they describe. The extent to which the field-names reflect their landscape is marked and is observable both in the use of individual elements, and in the language use of townships within the parish more broadly. The survey (Chapter Four) and glossary (Chapter Five) constitute a substantial contribution to the available field-name data for Cumberland, and for England more generally, supplementing the English Place-Name Society survey for Cumberland. Other key findings from this research (Chapter Seven) include the discovery of metaphorical elements unattested elsewhere, as well as other elements or element usages particular to the study area. Field-names which provide evidence for lost place-names, and instances of toponomastic overlap between England and Scotland, are observable within the data of this thesis; a lack of genitival -s in personal names within field-names is likewise notable. This thesis advocates for the development and implementation of a new field-name terminology model

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Classificação multiclasse de sinais de eletroencefalograma para tarefas de imaginação motora utilizando processamento estatístico de sinais e deep learning

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    Research Interests: Efficient classification of electroencephalogram (EEG) signals is crucial for the development of brain-computer interface systems. However, the complexity and variability of EEG signals pose significant challenges for accurate classification. Additionally, this study has social relevance as it can contribute to the development of assistive brain-computer interfaces, benefiting individuals with severe motor impairments, such as those who have experienced a stroke. These interfaces have the potential to improve the quality of life for these individuals by enabling communication and device control through brain activity. Objectives: This study aimed to compare the performance and computational cost of an artificial neural network using different signal processing techniques for the classification of resting state and left/right wrist movement imagination states from EEG signals. Three statistical signal processing techniques, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Singular Spectrum Analysis (SSA), were explored in conjunction with a Convolutional Neural Network (CNN) to enhance the classification of EEG signals. Results Obtained: The results revealed that the PCA technique led to a reduction in training time of up to 63.5% without significantly compromising performance in terms of classification accuracy. PCA proved to be a promising approach, capturing relevant information from the EEG signals and improving the CNN’s ability to classify accurately. On the other hand, both ICA and SSA techniques did not yield promising results. ICA had negative effects on feature extraction, resulting in decreased classification accuracy by the CNN. SSA, on the other hand, showed consistently low performance across all evaluated metrics, indicating challenges in capturing discriminative information from the EEG-IM signals.Interesses de pesquisa: A classificação eficiente dos sinais de eletroencefalograma (EEG) é fundamental para a construção de sistemas com interface cérebro-computador. No entanto, a complexidade dos sinais de EEG e sua variabilidade entre indivíduos apresentam desafios significativos para a classificação precisa. Este estudo tem relevância social, pois pode contribuir para o desenvolvimento de interfaces cérebro-computador assistivas, beneficiando pessoas com severos danos motores, como aquelas que sofreram acidente vascular cerebral (AVC). Essas interfaces têm o potencial de melhorar a qualidade de vida desses indivíduos, permitindo a comunicação e o controle de dispositivos através da atividade cerebral. Objetivos: Este estudo teve como objetivo comparar o desempenho e o custo computacional de uma rede neural artificial utilizando diferentes técnicas de processamento de sinal na classificação de estados de repouso e imaginação do movimento do punho esquerdo e direito a partir de sinais de EEG. Foram exploradas três técnicas estatísticas de processamento de sinais: Análise de Componentes Principais (PCA), Análise de Componentes Independentes (ICA) e Análise Espectral Singular (SSA), em conjunto com uma Rede Neural Convolucional (CNN). Resultados obtidos: Os resultados obtidos revelaram que a técnica de PCA proporcionou uma redução no tempo de treinamento de até 63,5%, sem comprometer significativamente o desempenho em termos de acurácia na classificação. A PCA demonstrou ser uma abordagem promissora, permitindo a captura de informações relevantes nos sinais de EEG e aprimorando a capacidade da CNN em realizar a classificação com precisão. Por outro lado, as técnicas de ICA e SSA não apresentaram resultados promissores. A ICA teve efeitos negativos na extração de características, resultando em uma diminuição na acurácia da classificação realizada pela CNN. A SSA, por sua vez, mostrou um desempenho geralmente baixo em todas as métricas avaliadas, indicando uma dificuldade em capturar as informações discriminativas presentes nos sinais de EEG-IM
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