Magnetoencephalography (MEG) is an invaluable functional brain imaging technique that provides direct, real-time monitoring of neuronal activity necessary for gaining insight into dynamic cortical networks. Our intentions with this book are to cover the richness and transdisciplinary nature of the MEG field, make it more accessible to newcomers and experienced researchers and to stimulate growth in the MEG area. The book presents a comprehensive overview of MEG basics and the latest developments in methodological, empirical and clinical research, directed toward master and doctoral students, as well as researchers. There are three levels of contributions: 1) tutorials on instrumentation, measurements, modeling, and experimental design; 2) topical reviews providing extensive coverage of relevant research topics; and 3) short contributions on open, challenging issues, future developments and novel applications. The topics range from neuromagnetic measurements, signal processing and source localization techniques to dynamic functional networks underlying perception and cognition in both health and disease. Topical reviews cover, among others: development on SQUID-based and novel sensors, multi-modal integration (low field MRI and MEG; EEG and fMRI), Bayesian approaches to multi-modal integration, direct neuronal imaging, novel noise reduction methods, source-space functional analysis, decoding of brain states, dynamic brain connectivity, sensory-motor integration, MEG studies on perception and cognition, thalamocortical oscillations, fetal and neonatal MEG, pediatric MEG studies, cognitive development, clinical applications of MEG in epilepsy, pre-surgical mapping, stroke, schizophrenia, stuttering, traumatic brain injury, post-traumatic stress disorder, depression, autism, aging and neurodegeneration, MEG applications in cognitive neuropharmacology and an overview of the major open-source analysis tools.
This is the first comprehensive introduction to MEG. Bringing together the leading researchers to provide the basic tools for planning and executing MEG experiments, as well as analyzing and interpreting the resulting data, it is a complete basic reference source for those interested in exploring or already using MEG.
This work provides newcomers and more experienced researchers with the very basics of magnetoencephalography (MEG) and electroencephalography (EEG)-two noninvasive methods that can inform about the neurodynamics of the human brain on a millisecond scale. These two closely related methods are addressed side by side, starting from their physical and physiological bases and then advancing to methods of data acquisition, analysis, visualization, and interpretation
|Author||: Roland Carlstedt|
|Release Date||: 2018-10-09|
|ISBN 10||: 1317528727|
|Pages||: 414 pages|
Out of the broad arena of sport science and sport psychology, Roland A. Carlstedt presents a comprehensive collection on the neuroscience and associated psychophysiology that underlies and drives sport performance. Featuring sections ranging from the basics and foundations (anatomy and physiology) to the applied (assessment during competition, training, and mental training), Handbook of Sport Neuroscience and Psychophysiology is the first volume to provide students, researchers, practitioners, and coaches the latest knowledge on the brain, mind-body processes, and psychophysiological responding in the context of sport performance.
In this book, the field of adaptive learning and processing is extended to arguably one of its most important contexts which is the understanding and analysis of brain signals. No attempt is made to comment on physiological aspects of brain activity; instead, signal processing methods are developed and used to assist clinical findings. Recent developments in detection, estimation and separation of diagnostic cues from different modality neuroimaging systems are discussed. These include constrained nonlinear signal processing techniques which incorporate sparsity, nonstationarity, multimodal data, and multiway techniques. Key features: Covers advanced and adaptive signal processing techniques for the processing of electroencephalography (EEG) and magneto-encephalography (MEG) signals, and their correlation to the corresponding functional magnetic resonance imaging (fMRI) Provides advanced tools for the detection, monitoring, separation, localising and understanding of functional, anatomical, and physiological abnormalities of the brain Puts a major emphasis on brain dynamics and how this can be evaluated for the assessment of brain activity in various states such as for brain-computer interfacing emotions and mental fatigue analysis Focuses on multimodal and multiway adaptive processing of brain signals, the new direction of brain signal research
|Author||: Kensuke Sekihara,Srikatan S. Nagarajan|
|Publisher||: Springer Science & Business Media|
|Release Date||: 2008-05-30|
|ISBN 10||: 3540793704|
|Pages||: 245 pages|
Neural activity in the human brain generates coherent synaptic and intracellular currents in cortical columns that create electromagnetic signals which can be measured outside the head using magnetoencephalography (MEG) and electroencephalography (EEG). Electromagnetic brain imaging refers to techniques that reconstruct neural activity from MEG and EEG signals. Electromagnetic brain imaging is unique among functional imaging techniques for its ability to provide spatio-temporal brain activation profiles that reflect not only where the activity occurs in the brain but also when this activity occurs in relation to external and internal cognitive events, as well as to activity in other brain regions. Adaptive spatial filters are powerful algorithms for electromagnetic brain imaging that enable high-fidelity reconstruction of neuronal activity. This book describes the technical advances of adaptive spatial filters for electromagnetic brain imaging by integrating and synthesizing available information and describes various factors that affect its performance. The intended audience include graduate students and researchers interested in the methodological aspects of electromagnetic brain imaging.
A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings.
With the rise of advanced computerized data collection systems, monitoring devices, and instrumentation technologies, large and complex datasets accrue as an inevitable part of biomedical enterprise. The availability of these massive amounts of data offers unprecedented opportunities to advance our understanding of underlying biological and physiological functions, structures, and dynamics. Biosignal Processing: Principles and Practices provides state-of-the-art coverage of contemporary methods in biosignal processing with an emphasis on brain signal analysis. After introducing the fundamentals, it presents emerging methods for brain signal processing, focusing on specific non-invasive imaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG), magnetic resonance imaging (MRI), and functional near-infrared spectroscopy (fNIR). In addition, the book presents recent advances, reflecting the evolution of biosignal processing. As biomedical datasets grow larger and more complicated, the development and use of signal processing methods to analyze and interpret these data has become a matter of course. This book is one step in the development of biosignal analysis and is designed to stimulate new ideas and opportunities in the development of cutting-edge computational methods for biosignal processing.
Magnetoencephalography (MEG) and electroencephalography (EEG) provide complementary views to the neurodynamics of healthy and diseased human brains. Both methods are totally noninvasive and can track with millisecond temporal resolution spontaneous brain activity, evoked responses to various sensory stimuli, as well as signals associated with the performance of motor, cognitive and affective tasks. MEG records the magnetic fields, and EEG the potentials associated with the same neuronal currents, which however are differentially weighted due to the physical and physiological differences between the methods. MEG is rather selective to activity in the walls of cortical folds, whereas EEG senses currents from the cortex (and brain) more widely, making it harder to pinpoint the locations of the source currents in the brain. Another important difference between the methods is that skull and scalp dampen and smear EEG signals, but do not affect MEG. Hence, to fully understand brain function, information from MEG and EEG should be combined. Additionally, the excellent neurodynamical information these two methods provide can be merged with data from other brain-imaging methods, especially functional magnetic resonance imaging where spatial resolution is a major strength. MEG-EEG Primer is the first-ever volume to introduce and discuss MEG and EEG in a balanced manner side-by-side, starting from their physical and physiological bases and then advancing to methods of data acquisition, analysis, visualization, and interpretation. The authors pay special attention to careful experimentation, guiding readers to differentiate brain signals from various artifacts and to assure that the collected data are reliable. The book weighs the strengths and weaknesses of MEG and EEG relative to one another and to other methods used in systems, cognitive, and social neuroscience. The authors also discuss the role of MEG and EEG in the assessment of brain function in various clinical disorders. The book aims to bring members of multidisciplinary research teams onto equal footing so that they can contribute to different aspects of MEG and EEG research and to be able to participate in future developments in the field.
|Author||: Luigi Landini,Vincenzo Positano,Maria Santarelli|
|Publisher||: CRC Press|
|Release Date||: 2018-10-03|
|ISBN 10||: 1420028669|
|Pages||: 632 pages|
The popularity of magnetic resonance (MR) imaging in medicine is no mystery: it is non-invasive, it produces high quality structural and functional image data, and it is very versatile and flexible. Research into MR technology is advancing at a blistering pace, and modern engineers must keep up with the latest developments. This is only possible with a firm grounding in the basic principles of MR, and Advanced Image Processing in Magnetic Resonance Imaging solidly integrates this foundational knowledge with the latest advances in the field. Beginning with the basics of signal and image generation and reconstruction, the book covers in detail the signal processing techniques and algorithms, filtering techniques for MR images, quantitative analysis including image registration and integration of EEG and MEG techniques with MR, and MR spectroscopy techniques. The final section of the book explores functional MRI (fMRI) in detail, discussing fundamentals and advanced exploratory data analysis, Bayesian inference, and nonlinear analysis. Many of the results presented in the book are derived from the contributors' own work, imparting highly practical experience through experimental and numerical methods. Contributed by international experts at the forefront of the field, Advanced Image Processing in Magnetic Resonance Imaging is an indispensable guide for anyone interested in further advancing the technology and capabilities of MR imaging.
Changes in the neurological functions of the human brain are often a precursor to numerous degenerative diseases. Advanced EEG systems and other monitoring systems used in preventive diagnostic procedures incorporate innovative features for brain monitoring functions such as real-time automated signal processing techniques and sophisticated amplifiers. Highlighting the US, Europe, Australia, New Zealand, Japan, Korea, China, and many other areas, EEG/ERP Analysis: Methods and Applications examines how researchers from various disciplines have started to work in the field of brain science, and explains the different techniques used for processing EEG/ERP data. Engineers can learn more about the clinical applications, while clinicians and biomedical scientists can familiarize themselves with the technical aspects and theoretical approaches. This book explores the recent advances involved in EEG/ERP analysis for brain monitoring, details successful EEG and ERP applications, and presents the neurological aspects in a simplified way so that those with an engineering background can better design clinical instruments. It consists of 13 chapters and includes the advanced techniques used for signal enhancement, source localization, data fusion, classification, and quantitative EEG. In addition, some of the chapters are contributed by neurologists and neurosurgeons providing the clinical aspects of EEG/ERP analysis. Covers a wide range of EEG/ERP applications with state-of-the-art techniques for denoising, analysis, and classification Examines new applications related to 3D display devices Includes MATLAB® codes EEG/ERP Analysis: Methods and Applications is a resource for biomedical and neuroscience scientists who are working on neural signal processing and interpretation, and biomedical engineers who are working on EEG/ERP signal analysis methods and developing clinical instrumentation. It can also assist neurosurgeons, psychiatrists, and postgraduate students doing research in neural engineering, as well as electronic engineers in neural signal processing and instrumentation.
Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain