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Brain Source Localization Using EEG Signal Analysis

by Munsif Ali Jatoi, Nidal Kamel


  • Covers brain source localization with specific clinical applications.

  • Encompasses material related to mathematical modeling for EEG analysis.

  • Incorporates and utilizes MATLAB where appropriate.

  • Includes a detailed overview of neuroimaging techniques such as fMRI and MEG

  • Features extensive bibliographies at the end of each chapter for further study


Of the research areas devoted to biomedical sciences, the study of the brain remains a field that continually attracts interest due to the vast range of people afflicted with debilitating brain disorders and those interested in ameliorating its effects. To discover the roots of maladies and grasp the dynamics of brain functions, researchers and practitioners often turn to a process known as brain source localization, which assists in determining the source of electromagnetic signals from the brain. Aiming to promote both treatments and understanding of brain ailments, ranging from epilepsy and depression to schizophrenia and Parkinson’s disease, the authors of this book provide a comprehensive account of current developments in the use of neuroimaging techniques for brain analysis. Their book addresses a wide array of topics, including EEG forward and inverse problems, the application of classical MNE, LORETA, Bayesian based MSP, and its modified version, M-MSP. Within the ten chapters that comprise this book, clinicians, researchers, and field experts concerned with the state of brain source localization will find a store of information that can assist them in the quest to enhance the quality of life for people living with brain disorders.


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Optical Imaging for Biomedical and Clinical Applications

by Ahmad Fadzil Mohamad Hani, Dileep Kumar


  • Provides an in-depth introduction to the optical imaging and medial field, exploring applications for biomedical research, describes fundamentals of methodologies and algorithms

  • Examines state-of-the-art techniques used for in vivo optical imaging in experimental research on skin, wound and ophthalmology

  • Presents state-of-the-art methodologies and algorithms such as Monte Carlo, ICA, PCA, Fuzzy c-means, RETINO, RETINEX, Time Domain Constraint Estimator (TDCE) and Linear Discriminant Analysis (LDA)

  • Contains real-world case studies and demonstrates implementation of real imaging data acquired from various hospitals


Optical imaging is a rapidly emerging imaging technique that has been successfjully translated into biomedical applications ranging from clinical diagnosis to molecular biology. This book includes an introductory section to explore various optical imaging devices and their functionality and roles for biomedical applications such as dermatology and ophthalmology. Recent developments as exemplified with the authors research are explored in detail. In depth discussion of other disease conditions and their diagnosis with optical imaging techniques are also covered.


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Designing EEG Experiments for Studying the Brain : Design Code and Example Datasets

by Aamir Saeed Malik (Author), Hafeez Ullah Amin (Author) 

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.

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Surface Imaging for Biomedical Applications

by Ahmad Fadzil Mohamad Hani (Editor)

Based on hospital clinical trials examining the use of signal and image processing techniques, Surface Imaging for Biomedical Applications bridges the gap between engineers and clinicians. This text offers a thorough analysis of biomedical surface imaging to medical practitioners as it relates to the diagnosis, detection, and monitoring of skin conditions and disease. Written from an engineer's perspective, the book discusses image acquisition methods, image processing, and pattern recognition techniques. It focuses on a variety of techniques used in recent years for image processing and pattern recognition (principal component analysis, independent component analysis, singular value decomposition, texture modeling, inverse model analysis, polynomial surface fitting, and classification techniques), and considers interventional and non-invasive procedures used to diagnose skin-related disease.

It examines the biological causation of four skin disorders (psoriasis, vitiligo, ulcer, and acne), provides basic terminologies in surface imaging, and details the outcome of various clinical observations and other research. It also details numerous measurement parameters related to surface imaging (body surface, skin color, tissue characteristic, thickness, roughness, volume of skin, and retinal changes).

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EEG/ERP Analysis: Methods and Applications

by Kamel Nidal (Editor),  Aamir Saeed Malik (Editor)

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.


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Depth Map and 3D Imaging Applications: Algorithms and Technologies 

by Aamir Saeed Malik (Author, Editor),  Tae Sun Choi (Editor),  Humaira Nisar (Editor)

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



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CPU-based Application Transformation to CUDA: Transformation of CPU-based Applications To Leverage on Graphics Processors using CUDA 

by Anas Mohd Nazlee (Author),  Fawnizu Azmadi Hussin (Author)

Scientific computation requires a great amount of computing power especially in floating-point operation but a high-end multi-cores processor is currently limited in terms of floating point operation performance and parallelization. Recent technological advancement has made parallel computing technically and financially feasible using Compute Unified Device Architecture (CUDA) developed by NVIDIA. This research focuses on measuring the performance of CUDA and implementing CUDA for a scientific computation involving the process of porting the source code from CPU to GPU using direct integration technique. The ported source code is then optimized by managing the resources to achieve performance gain over CPU. It is found that CUDA is able to boost the performance of the system up to 69 times in Parboil Benchmark Suite. Successful attempt at porting Serpent encryption algorithm and Lattice Boltzmann Method provided up to 7 times throughput performance gain and up to 10 times execution time performance gain respectively over the CPU. Direct integration guideline for porting the source code is then produced based on the two implementations.


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An Improved Markov Random Field Design Approach For Digital Circuits: Introducing Fault-Tolerance With Higher Noise-Immunity For The Nano-Circuits As Compared To CMOS And MRF Designs 

by Jahanzeb Anwer (Author),  Nor Hisham Bin Hamid (Author),  Vijanth Sagayan Asirvadam (Author)

As the MOSFET dimensions scale down to nanoscale level, the reliability of circuits based on these devices decreases. Therefore, a mechanism has to be devised that can make the nanoscale systems perform reliably using unreliable circuit components. The solution is fault-tolerant circuit design. Markov Random Field (MRF) is an effective approach that achieves fault-tolerance in integrated circuit design. The previous research on this technique suffers from limitations at the design, simulation and implementation levels. As improvements, the MRF fault-tolerance rules have been validated for a practical circuit example. The simulation framework is extended from thermal to a combination of thermal and random telegraph signal noise sources to provide a more rigorous noise environment for the simulation of nanoscale circuits. Moreover, an architecture-level improvement has been proposed in the design of previous MRF gates. The re-designed MRF is termed as Improved-MRF. By simulating various test circuits in Cadence, it is found that Improved-MRF circuits are 400 whereas MRF circuits are only 10 times more noise-tolerant than the CMOS alternatives.


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