A Study of Brain Networks Associated With Motor Sequence Learning Foot Tapping Tasks

Understanding the learning behavior of brain functional connectivity within motor sequence learning of foot tapping tasks is of great importance to help improving walking quality of elderly people. It is also of great interest to clinical and scientific communities. The role of functional connectivity in brain function has not yet been well understood. This study comprises of two parts; the first part is to investigate brain signal stationarity, while the other will look into brain interactions while performing motor learning sequence task. Functional magnetic resonance imaging (fMRI) was utilized to acquire data from twelve healthy adult participants to study brain functional connectivity and interactions during a sequence of motor learning foot tapping tasks. Tasks were divided into: two different learning blocks, two control blocks, and five blocks of resting states. In this condition, 90 percent of the subjects developed awareness of the sequence. The stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks. The reverse arrangement test RAT was utilized to investigate the time series stationarity. Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. Next, we choose to apply psycho-physiological interactions (PPI) approach to our data and we revealed some information about the degree to which components of large-scale neural systems were functionally coupled together to achieve and perform the designed learning sequence task. In this work, we will introduce the idea of psycho-physiological interaction (PPI), which explains the responses in one cortical region in terms of an interaction between the effects of other regions and learning task parameter. We have found that the Thalamus was mostly involved and modulated with our predesigned motor learning task. We also found that the Middle frontal Gyrus and Left pre-central Gyrus were the most interacting regions with the above mentioned cluster. This interaction can only be related to the interactions that are based on the experimental factors which are the psychological and physiological interactions. The current results have also supported PPI as a potential tool for understanding learning mechanism during foot tapping tasks

Treadmill training augmented with real-time visualisation feedback and function electrical stimulation for gait rehabilitation after stroke : a feasibility study

Motor rehabilitation typically requires patients to perform task-specific training, in which biofeedback can be instrumental for encouraging neuroplasticity after stroke. Treadmill training augmented with real-time visual feedback and functional electrical stimulation (FES) may have a beneficial synergistic effect on this process. This study aims to develop a multi-channel FES (MFES) system with stimulation triggers based on the phase of gait cycle, determined using a 3D motion capture system. A feasibility study was conducted to determine whether this enhanced treadmill gait training systemis suitable for stroke survivors in clinical practice. The real-time biomechanical visual feedback system with computerised MFES was developed using six motion-capture cameras installed around a treadmill.;This system was designed to stimulate the pretibial muscle for correcting foot drop problems, gastro-soleus for facilitating push-off, and quadriceps and hamstring for improving knee stability. Dynamic avatar movement and step length/ratio were displayed on a monitor, providing patients with real-time visual biofeedback. Participants received up to 20 minutes of enhanced treadmill training once or twice per week for 6 weeks. Training programme, pre- and post-training ability, and adverse events of each participant were recorded. Feedback was also collected from participants and physiotherapists regarding their experience. Eight out of ten participants fully completed their programme.;In total, 67 training sessions were carried out. All participants had a good attendance rate. The number and duration of training sessions ranged from 5 to 20, and 11 to 20 minutes, respectively. The MFES system successfully improved gait patterns during training, and feedback from participants and physiotherapists regarding their experience of the research intervention was overwhelmingly positive. In conclusion, this enhanced treadmill gait training system is feasible for use in gait rehabilitation after stroke. However, a well-designed clinical trial with a larger sample size is needed to determine clinical efficacy on gait recovery.Motor rehabilitation typically requires patients to perform task-specific training, in which biofeedback can be instrumental for encouraging neuroplasticity after stroke. Treadmill training augmented with real-time visual feedback and functional electrical stimulation (FES) may have a beneficial synergistic effect on this process. This study aims to develop a multi-channel FES (MFES) system with stimulation triggers based on the phase of gait cycle, determined using a 3D motion capture system. A feasibility study was conducted to determine whether this enhanced treadmill gait training systemis suitable for stroke survivors in clinical practice. The real-time biomechanical visual feedback system with computerised MFES was developed using six motion-capture cameras installed around a treadmill.;This system was designed to stimulate the pretibial muscle for correcting foot drop problems, gastro-soleus for facilitating push-off, and quadriceps and hamstring for improving knee stability. Dynamic avatar movement and step length/ratio were displayed on a monitor, providing patients with real-time visual biofeedback. Participants received up to 20 minutes of enhanced treadmill training once or twice per week for 6 weeks. Training programme, pre- and post-training ability, and adverse events of each participant were recorded. Feedback was also collected from participants and physiotherapists regarding their experience. Eight out of ten participants fully completed their programme.;In total, 67 training sessions were carried out. All participants had a good attendance rate. The number and duration of training sessions ranged from 5 to 20, and 11 to 20 minutes, respectively. The MFES system successfully improved gait patterns during training, and feedback from participants and physiotherapists regarding their experience of the research intervention was overwhelmingly positive. In conclusion, this enhanced treadmill gait training system is feasible for use in gait rehabilitation after stroke. However, a well-designed clinical trial with a larger sample size is needed to determine clinical efficacy on gait recovery

Modeling speech processing in case of neurogenic speech and language disorders: neural dysfunctions, brain lesions, and speech behavior

Computer-implemented neural speech processing models can simulate patients suffering from neurogenic speech and language disorders like aphasia, dysarthria, apraxia of speech, and neurogenic stuttering. Speech production and perception tasks simulated by using quantitative neural models uncover a variety of speech symptoms if neural dysfunctions are inserted into these models. Neural model dysfunctions can be differentiated with respect to type (dysfunction of neuron cells or of neural connections), location (dysfunction appearing in a specific buffer of submodule of the model), and severity (percentage of affected neurons or neural connections in that specific submodule of buffer). It can be shown that the consideration of quantitative computer-implemented neural models of speech processing allows to refine the definition of neurogenic speech disorders by unfolding the relation between inserted neural dysfunction and resulting simulated speech behavior while the analysis of neural deficits (e.g., brain lesions) uncovered from imaging experiments with real patients does not necessarily allow to precisely determine the neurofunctional deficit and thus does not necessarily allow to give a precise neurofunctional definition of a neurogenic speech and language disorder. Furthermore, it can be shown that quantitative computer-implemented neural speech processing models are able to simulate complex communication scenarios as they appear in medical screenings, e.g., in tasks like picture naming, word comprehension, or repetition of words or of non-words (syllable sequences) used for diagnostic purposes or used in speech tasks appearing in speech therapy scenarios (treatments). Moreover, neural speech processing models which can simulate neural learning are able to simulate progress in the overall speech processing skills of a model (patient) resulting from specific treatment scenarios if these scenarios can be simulated. Thus, quantitative neural models can be used to sharpen up screening and treatment scenarios and thus increase their effectiveness by varying certain parameters of screening as well as of treatment scenarios

Brain-Inspired Computing

This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network development. The changes were to include evaluation tools that can be applied to neural networks at each phase of the software engineering life cycle. The result was a formal evaluation approach to increase the product quality of systems that use neural networks for their implementation

Deep learning in mining biological data

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorised in three broad types (i.e., images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data intensive machine learning techniques. Artificial neural network based learning systems are well known for their pattern recognition capabilities and lately their deep architectures - known as deep learning (DL) - have been successfully applied to solve many complex pattern recognition problems. To investigate how DL - especially its different architectures - has contributed and utilised in the mining of biological data pertaining to those three types, a meta analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures' applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

European governance challenges in bio-engineering : making perfect life : bio-engineering (in) the 21st century : final report

In the STOA project Making Perfect Life four fields were studied of 21st century bio-engineering: engineering of living artefacts, engineering of the body, engineering of the brain, and engineering of intelligent artefacts. This report describes the main results of the project. It shows how developments in the four fields of bio-engineering are shaped by two megatrends: "biology becoming technology" and "technology becoming biology". These developments result in a broadening of the bio-engineering debate in our society. The report addresses the long term views that are inspiring this debate and discusses a multitude of ethical, legal and social issues that arise from bio-engineering developments in the fields described. Against this background four specific developments are studied in more detail: the rise of human genome sequencing, the market introduction of neurodevices, the capturing by information technology of the psychological and physiological states of users, and the pursuit of standardisation in synthetic biology. These developments are taken in this report as a starting point for an analysis of some of the main European governance challenges in 21st century bio-engineering