EEG Signal Processing and Machine Learning Explore cutting edge
techniques at the forefront of electroencephalogram research and
artificial intelligence from leading voices in the field The newly
revised Second Edition of EEG Signal Processing and Machine
Learning delivers an inclusive and thorough exploration of new
techniques and outcomes in electroencephalogram (EEG) research in
the areas of analysis, processing, and decision making about a
variety of brain states, abnormalities, and disorders using
advanced signal processing and machine learning techniques. The
book content is substantially increased upon that of the first
edition and, while it retains what made the first edition so
popular, is composed of more than 50% new material. The
distinguished authors have included new material on tensors for EEG
analysis and sensor fusion, as well as new chapters on mental
fatigue, sleep, seizure, neurodevelopmental diseases, BCI, and
psychiatric abnormalities. In addition to including a comprehensive
chapter on machine learning, machine learning applications have
been added to almost all the chapters. Moreover, multimodal brain
screening, such as EEG-fMRI, and brain connectivity have been
included as two new chapters in this new edition. Readers will also
benefit from the inclusion of: A thorough introduction to EEGs,
including neural activities, action potentials, EEG generation,
brain rhythms, and EEG recording and measurement An exploration of
brain waves, including their generation, recording, and
instrumentation, abnormal EEG patterns and the effects of ageing
and mental disorders A treatment of mathematical models for normal
and abnormal EEGs Discussions of the fundamentals of EEG signal
processing, including statistical properties, linear and nonlinear
systems, frequency domain approaches, tensor factorization,
diffusion adaptive filtering, deep neural networks, and
complex-valued signal processing Perfect for biomedical engineers,
neuroscientists, neurophysiologists, psychiatrists, engineers,
students and researchers in the above areas, the Second Edition of
EEG Signal Processing and Machine Learning will also earn a place
in the libraries of undergraduate and postgraduate students
studying Biomedical Engineering, Neuroscience and Epileptology.
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