Over the last decade, significant progress has been made in 3D imaging research. As a result, 3D imaging methods and techniques are being employed for various applications, including 3D television, intelligent robotics, medical imaging, and stereovision. Depth Map and 3D Imaging Applications: Algorithms and Technologies present various 3D algorithms developed in the recent years and to investigate the application of 3D methods in various domains. Containing five sections, this book offers perspectives on 3D imaging algorithms, 3D shape recovery, stereoscopic vision and autostereoscopic vision, 3D vision for robotic applications, and 3D imaging applications. This book is an important resource for professionals, scientists, researchers, academics, and software engineers in image/video processing and computer vision.

EEG-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis introduces EEG-based machine learning solutions for diagnosis and assessment of treatment efficacy for a variety of conditions. With a unique combination of background and practical perspectives for the use of automated EEG methods for mental illness, it details for readers how to design a successful experiment, providing experiment designs for both clinical and behavioral applications. This book details the EEG-based functional connectivity correlates for several conditions, including depression, anxiety, and epilepsy, along with pathophysiology of depression, underlying neural circuits and detailed options for diagnosis. It is a necessary read for those interested in developing EEG methods for addressing challenges for mental illness and researchers exploring automated methods for diagnosis and objective treatment assessment.

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 (R) 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.

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 (R) 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.

Designing EEG Experiments for Studying the Brain: Design Code and Example Datasets details the design of various brain experiments using electroencephalogram (EEG). Providing guidelines for designing an EEG experiment, it is primarily for researchers who want to venture into this field by designing their own experiments as well as those who are excited about neuroscience and want to explore various applications related to the brain. The first chapter describes how to design an EEG experiment and details the various parameters that should be considered for success, while remaining chapters provide experiment design for a number of neurological applications, both clinical and behavioral. As each chapter is accompanied with experiment design codes and example datasets, those interested can quickly design their own experiments or use the current design for their own purposes. Helpful appendices provide various forms for one's experiment including recruitment forms, feedback forms, ethics forms, and recommendations for related hardware equipment and software for data acquisition, processing, and analysis.