This book presents fundamental requirements, electrical specification, and parameter tradeoffs of wearable EEG acquisition circuits, especially those compatible with dry electrodes for user-friendly recordings. The authors introduce active electrode, the most promising solution for dry electrodes-based EEG measurement. This architectural concept has been combined with various, innovative circuit design techniques to illustrate structured IC design methodologies for high performance EEG recording. This book also gives examples on the design, implementation and evaluation of three generations of active electrode ICs.

This book provides the most comprehensive and consistent survey of the field of IC design for Biological Sensing and Processing. The authors describe a multitude of applications that require custom CMOS IC design and highlight the techniques in analog and mixed-signal circuit design that potentially can cross boundaries and benefit the very wide community of bio-medical engineers.

Biopotential Readout Circuits for Portable Acquisition Systems describes one of the main building blocks of such miniaturized biomedical signal acquisition systems. The focus of this book is on the implementation of low-power and high-performance integrated circuit building blocks that can be used to extract biopotential signals from conventional biopotential electrodes. New instrumentation amplifier architectures are introduced and their design is described in detail. These amplifiers are used to implement complete acquisition demonstrator systems that are a stepping stone towards practical miniaturized and low-power systems.

This thesis presents the design, development, and experimental characterization of wireless integrated circuits and systems built for ambulatory electroencephalography (EEG) monitoring and diagnostics. Two prototypes are presented. The first one is a 3x4 mm2 integrated circuit (IC), designed and fabricated in a 130nm CMOS technology, and integrates eight recording channels. A novel analog circuit is employed in each channel that detects and removes motion artifacts during amplification, resulting in a clean EEG prior to digitization. The idea prevents the amplifier saturation and removes the need for post processing of the digitized signals using artifact pattern recognition algorithms. The second prototype is an integrated system in the form of a fully-flexible wearable wireless medical device. It integrates eight motion-resilient, active-electrode recording channels, with inter-digitated non-contact electrodes embedded into each of them, all implemented on a 4-layer polyimide flexible substrate, yielding the smallest form factor reported for a wearable EEG device. The flexible main board is connected to a 13x17 mm2 rigid printed circuit board that hosts a low-power FPGA and a BLE 5.0 transceiver, which add diagnostic capability and wireless operation features to the device, respectively. The entire wearable solution with the battery weighs 9.2 grams.

Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors.

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. Written to assist neuroscientists in experiment designs using EEG Presents a step-by-step approach to designing both clinical and behavioral EEG experiments Includes experiment design codes and example datasets Provides inclusion and exclusion criteria to help correctly identify experiment subjects and the minimum number of samples Includes appendices that provide recruitment forms, ethics forms, and various subjective tests associated with each of the chapters