Inside the brain of an elite athlete: The neural processes that support high achievement in sports

Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance

Advancements in Sensor Technologies and Control Strategies for Lower-Limb Rehabilitation Exoskeletons: A Comprehensive Review

Lower-limb rehabilitation exoskeletons offer a transformative approach to enhancing recovery in patients with movement disorders affecting the lower extremities. This comprehensive systematic review delves into the literature on sensor technologies and the control strategies integrated into these exoskeletons, evaluating their capacity to address user needs and scrutinizing their structural designs regarding sensor distribution as well as control algorithms. The review examines various sensing modalities, including electromyography (EMG), force, displacement, and other innovative sensor types, employed in these devices to facilitate accurate and responsive motion control. Furthermore, the review explores the strengths and limitations of a diverse array of lower-limb rehabilitation-exoskeleton designs, highlighting areas of improvement and potential avenues for further development. In addition, the review investigates the latest control algorithms and analysis methods that have been utilized in conjunction with these sensor systems to optimize exoskeleton performance and ensure safe and effective user interactions. By building a deeper understanding of the diverse sensor technologies and monitoring systems, this review aims to contribute to the ongoing advancement of lower-limb rehabilitation exoskeletons, ultimately improving the quality of life for patients with mobility impairments

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Adaptive Cognitive Interaction Systems

Adaptive kognitive Interaktionssysteme beobachten und modellieren den Zustand ihres Benutzers und passen das Systemverhalten entsprechend an. Ein solches System besteht aus drei Komponenten: Dem empirischen kognitiven Modell, dem komputationalen kognitiven Modell und dem adaptiven Interaktionsmanager. Die vorliegende Arbeit enthält zahlreiche Beiträge zur Entwicklung dieser Komponenten sowie zu deren Kombination. Die Ergebnisse werden in zahlreichen Benutzerstudien validiert

Self-Organized Supercriticality and Oscillations in Networks of Stochastic Spiking Neurons

Networks of stochastic spiking neurons are interesting models in the area of Theoretical Neuroscience, presenting both continuous and discontinuous phase transitions. Here we study fully connected networks analytically, numerically and by computational simulations. The neurons have dynamic gains that enable the network to converge to a stationary slightly supercritical state (self-organized supercriticality or SOSC) in the presence of the continuous transition. We show that SOSC, which presents power laws for neuronal avalanches plus some large events, is robust as a function of the main parameter of the neuronal gain dynamics. We discuss the possible applications of the idea of SOSC to biological phenomena like epilepsy and dragon king avalanches. We also find that neuronal gains can produce collective oscillations that coexists with neuronal avalanches, with frequencies compatible with characteristic brain rhythms.Comment: 16 pages, 16 figures divided into 7 figures in the articl

NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

Deep learning classification model of mental workload levels using EEG signals

Understanding and improving humance performance, especially in situations that require safety, productivity, and well-being, relies on categorising mental workload (MWL). Traditional methods for measuring MWL, such as in driving and piloting, have given us some understanding, but these methods must accurately distinguish between low and high workload levels. Excessive work can tyre participants, while insufficient work can make them bored and inefficient. Traditional MWL assessment tools, such as questionnaires, sometimes make it harder for people to manage their MWL, especially when they struggle to express or understand their thoughts and feelings. The recent work shift to neurophysiological signals, specifically electroencephalogram (EEG), provides a promising way to measure brain activity related to MWL non-invasively. Advanced techniques such as deep learning have made it easier to study EEG signals in more detail. Our goal was to develop a clear and consistent approach for using EEG signals to classify MWL effectively. Our approach focused on each process stage, from preparing the data to evaluating the model and addressing common mistakes and misunderstandings in current techniques. The first study addresses the challenges of using EEG data contaminated by artefacts for assessing MWL. EEG signal artefacts, such as eye movement or muscle activity, can skew MWL assessment. Recently, there has been significant progress in using deep learning models to interpret EEG signals, but the challenge remains. The preprocessing pipeline for EEG artefact removal is broad and inconsistently adopted; some pipelines are time-consuming and contain human intervention steps, so they are unsuitable for automation systems. Therefore, this study focused on automatic EEG artefact removal for deep learning analysis. Furthermore, we examined the impact of various preprocessing techniques on the effectiveness of deep learning models in classifying MWL levels. We used state-of-the-art models such as Stacked LSTM, BLSTM, and BLSTM-LSTM, and found that certain techniques—specifically, the ADJUST algorithm—significantly enhanced model performance. However, the sophisticated models could extract relevant information from raw data, indicating a reduced need for preprocessing. The second study shifted the focus to channel selection to refine the automation of MWL classification and reduce unnecessary computational expenses by using unnecessary electrodes, aligning more closely to real-world applications. We prioritised the best electrode setup focusing on brain activity related to MWL. We removed unnecessary data using Riemannian geometry, an effective method for EEG channel selection. We aimed to balance information sufficiency with computational efficiency and to reduce the number of electrodes. The study also evaluated covariance estimators for Riemannian geometry for their effectiveness in channel selection and impact on deep learning models for MWL classification, as the traditional Empirical Covariance (EC) has limitations for the EEG signal. Finally, the third study tackled a critical but frequently overlooked aspect of MWL level classification using machine learning or deep learning techniques: the temporal nature of EEG signals. We underscored that the traditional cross-validation technique violates the sequential nature of time series data, leading to data leakage, model overfitting, and inaccurate MWL assessment. Specifically, to predict the subject’s MWL level, we could not randomly split data and use future data to train the model and predict the previous MWL level. To address this problem, this study focused on the model training phase, specifically on the importance of time series cross-validation methods. We adopted the expanding window and rolling window strategies, finding that using the expanding window strategy outperformed those using the rolling window strategy. This research carefully developed a comprehensive and consistent method for classifying MWL using EEG signals. We aimed to correct misunderstandings and set a standard in brain-computer interface (BCI) systems. This will help guide future research and development efforts.Understanding and improving humance performance, especially in situations that require safety, productivity, and well-being, relies on categorising mental workload (MWL). Traditional methods for measuring MWL, such as in driving and piloting, have given us some understanding, but these methods must accurately distinguish between low and high workload levels. Excessive work can tyre participants, while insufficient work can make them bored and inefficient. Traditional MWL assessment tools, such as questionnaires, sometimes make it harder for people to manage their MWL, especially when they struggle to express or understand their thoughts and feelings. The recent work shift to neurophysiological signals, specifically electroencephalogram (EEG), provides a promising way to measure brain activity related to MWL non-invasively. Advanced techniques such as deep learning have made it easier to study EEG signals in more detail. Our goal was to develop a clear and consistent approach for using EEG signals to classify MWL effectively. Our approach focused on each process stage, from preparing the data to evaluating the model and addressing common mistakes and misunderstandings in current techniques. The first study addresses the challenges of using EEG data contaminated by artefacts for assessing MWL. EEG signal artefacts, such as eye movement or muscle activity, can skew MWL assessment. Recently, there has been significant progress in using deep learning models to interpret EEG signals, but the challenge remains. The preprocessing pipeline for EEG artefact removal is broad and inconsistently adopted; some pipelines are time-consuming and contain human intervention steps, so they are unsuitable for automation systems. Therefore, this study focused on automatic EEG artefact removal for deep learning analysis. Furthermore, we examined the impact of various preprocessing techniques on the effectiveness of deep learning models in classifying MWL levels. We used state-of-the-art models such as Stacked LSTM, BLSTM, and BLSTM-LSTM, and found that certain techniques—specifically, the ADJUST algorithm—significantly enhanced model performance. However, the sophisticated models could extract relevant information from raw data, indicating a reduced need for preprocessing. The second study shifted the focus to channel selection to refine the automation of MWL classification and reduce unnecessary computational expenses by using unnecessary electrodes, aligning more closely to real-world applications. We prioritised the best electrode setup focusing on brain activity related to MWL. We removed unnecessary data using Riemannian geometry, an effective method for EEG channel selection. We aimed to balance information sufficiency with computational efficiency and to reduce the number of electrodes. The study also evaluated covariance estimators for Riemannian geometry for their effectiveness in channel selection and impact on deep learning models for MWL classification, as the traditional Empirical Covariance (EC) has limitations for the EEG signal. Finally, the third study tackled a critical but frequently overlooked aspect of MWL level classification using machine learning or deep learning techniques: the temporal nature of EEG signals. We underscored that the traditional cross-validation technique violates the sequential nature of time series data, leading to data leakage, model overfitting, and inaccurate MWL assessment. Specifically, to predict the subject’s MWL level, we could not randomly split data and use future data to train the model and predict the previous MWL level. To address this problem, this study focused on the model training phase, specifically on the importance of time series cross-validation methods. We adopted the expanding window and rolling window strategies, finding that using the expanding window strategy outperformed those using the rolling window strategy. This research carefully developed a comprehensive and consistent method for classifying MWL using EEG signals. We aimed to correct misunderstandings and set a standard in brain-computer interface (BCI) systems. This will help guide future research and development efforts