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 aspects.

Focuses on selected applications of neuroscience/cardiovascular/muscle-related biomedical areas.

Provides a machine learning update to a classical biomedical signal processing approach.

Explains deep learning and application to biomedical signal processing and analysis.

Explores relevant biomedical engineering and neuroscience state-of-the-art applications.

This book is intended for researchers and graduate students in biomedical signal processing, electrical engineering, neuroscience, and computer science.