A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.

A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.

This book offers the first, comprehensive guide to planning and conducting intracranial EEG studies, and analyzing intracranial EEG data. The chapters address core questions in the field of intracranial EEG research. They are written by internationally recognized experts in the domain of intracranial EEG and acknowledge the heterogeneity of approaches in this field. The particular format of the book allows readers to find clear guidelines, hands-on expertise and invaluable background information for planning and conducting state-of-the-art intracranial EEG research projects. Besides offering a reference guide to newcomers in the field, it also provides scholarly information for the more experienced researcher and inspiration for the expert. The book covers a wide range of topics, with a special emphasis on aspects in which intracranial EEG data differ from other types of data in the cognitive neurosciences. It discusses typical patient characteristics and implantation schemes, ethical issues, and practical considerations for planning and running intracranial EEG experiments. It addresses signal characteristics and the physiological background of oscillatory and non-oscillatory aspects of intracranial EEG signals. It describes complex pre-processing steps such as advantages and disadvantages of different referencing schemes, and how to identify the location of electrodes. In addition, it answers specific questions on data processing, addressing core aspects of statistical analysis, and suggesting guidelines for data presentation. Further, it covers advanced topics such as causal interventions (i.e. deep brain stimulation), acquisition and analysis of single-unit data and multimodal recordings, and discusses important future challenges and opportunities in the field of intracranial EEG research.

V. Methodology: E. J. Wagenmakers (Volume Editor) Topics covered include methods and models in categorization; cultural consensus theory; network models for clinical psychology; response time modeling; analyzing neural time series data; models and methods for reinforcement learning; convergent methods of memory research; theories for discriminating signal from noise; bayesian cognitive modeling; mathematical modeling in cognition and cognitive neuroscience; the stop-signal paradigm; hypothesis testing and statistical inference; model comparison in psychology; fmri; neural recordings; open science; neural networks and neurocomputational modeling; serial versus parallel processing; methods in psychophysics.

An authoritative and in-depth treatment of state space methods, with a range of applications in neural and clinical data.

Biomechanics and Gait Analysis presents a comprehensive book on biomechanics that focuses on gait analysis. It is written primarily for biomedical engineering students, professionals and biomechanists with a strong emphasis on medical devices and assistive technology, but is also of interest to clinicians and physiologists. It allows novice readers to acquire the basics of gait analysis, while also helping expert readers update their knowledge. The book covers the most up-to-date acquisition and computational methods and advances in the field. Key topics include muscle mechanics and modeling, motor control and coordination, and measurements and assessments. This is the go to resource for an understanding of fundamental concepts and how to collect, analyze and interpret data for research, industry, clinical and sport.

The use of electroencephalography (EEG) to study the human mind has seen tremendous growth across a vast array of disciplines due to increased ease of use and affordability of the technology. Typically, researchers study how the magnitude of the waves changes over time or how the rhythm (frequency) of the waves changes over time. The Oxford Handbook of EEG Frequency is arguably the first book to comprehensively describe the ways to study how the frequency of the waves changes over time and how changes in frequency are linked to cognitive, affective, and motor processes. Consisting of 23 chapters written by leading authorities in the field, the book is separated into three sections, with the first focusing on the basics of EEG frequency research, linking frequency analyses to core components of EEG research with event-related potential (ERP) components and local field potentials (LFPs) in non-human animals. The second section looks at specific EEG frequency components that are commonly studied using traditional frequency bands of activity to study specific psychological processes. Finally, the third section explores EEG frequency analyses in special populations and altered states. Each chapter provides a diverse perspective on the topic, giving readers the opportunity to learn about a vast array of methods to conduct EEG frequency analyses, from 'traditional' to cutting-edge techniques, providing a comprehensive and in-depth overview of electroencephalography (EEG).

This book discusses communications technologies used in the field of healthcare, including IoT, soft computing, machine learning, big data, augmented reality, and wearable sensors. The book presents various applications that are helpful for research scholars and scientists who are working toward identifying and pinpointing the potential of this technology. The book also helps researchers and practitioners to understand and analyze the e-healthcare architecture through IoT and the state-of-the-art in IoT countermeasures with real-time challenges. Topics of interest include healthcare systems based on advanced development boards, mobile health parameters recording and monitoring systems, remote health / patient monitoring, hospital operations management, abnormality / disease detection by IoT devices, and efficient drug management. The book is relevant to a range of researchers, academics, and practitioners working on the intersection of IoT and healthcare.

The book provides insights from the 2nd International Conference on Communication, Computing and Networking organized by the Department of Computer Science and Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India on March 29–30, 2018. The book includes contributions in which researchers, engineers, and academicians as well as industrial professionals from around the globe presented their research findings and development activities in the field of Computing Technologies, Wireless Networks, Information Security, Image Processing and Data Science. The book provides opportunities for the readers to explore the literature, identify gaps in the existing works and propose new ideas for research.