Ganesh Naik – författare

Advanced Electroencephalography Analytical Methods: Fundamentals, Acquisition, and Applications presents the theoretical basis and applications of electroencephalography (EEG) signals in neuroscience, involving signal analysis, processing, signal acquisition, representation, and applications of EEG signal analysis using non-linear approaches and machine learning. It explains principles of neurophysiology, linear signal processing, computational intelligence, and the nature of signals including machine learning. Applications involve computer-aided diagnosis, brain-computer interfaces, rehabilitation engineering, and applied neuroscience.This book:Includes a comprehensive review on biomedical signals nature and acquisition aspectsFocuses on selected applications of neuroscience/cardiovascular/muscle-related biomedical areasProvides a machine learning update to a classical biomedical signal processing approachExplains deep learning and application to biomedical signal processing and analysisExplores relevant biomedical engineering and neuroscience state-of-the-art applicationsThis book is intended for researchers and graduate students in biomedical signal processing, electrical engineering, neuroscience, and computer science.

This book reports on the latest advances in the study of biomedical signal processing, and discusses in detail a number of open problems concerning clinical, biomedical and neural signals. It methodically collects and presents in a unified form the research findings previously scattered throughout various scientific journals and conference proceedings. In addition, the chapters are self-contained and can be read independently. Accordingly, the book will be of interest to university researchers, R&D engineers and graduate students who wish to learn the core principles of biomedical signal analysis, algorithms, and applications, while also offering a valuable reference work for biomedical engineers and clinicians who wish to learn more about the theory and recent applications of neural engineering and biomedical signal processing.