EEG Signal Processing and Machine Learning. Saeid Sanei

University of Leicester, UK

      This second edition first published 2022

      © 2022 John Wiley & Sons Ltd

      Edition History John Wiley & Sons, Ltd. (1e, 2007)

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       Library of Congress Cataloging‐in‐Publication Data

      Names: Sanei, Saeid, author. | Chambers, Jonathon A., author. | John Wiley & Sons, publisher

      Title: EEG signal processing and machine learning / Saeid Sanei, Jonathon A. Chambers.

      Description: Second edition. | Hoboken, NJ : Wiley, 2021. | Includes bibliographical references and index.

      Identifiers: LCCN 2021003276 (print) | LCCN 2021003277 (ebook) | ISBN 9781119386940 (hardback) | ISBN 9781119386926 (adobe pdf) | ISBN 9781119386933 (epub)

      Subjects: LCSH: Electroencephalography. | Signal processing. | Machine learning.

      Classification: LCC RC386.6.E43 S252 2021 (print) | LCC RC386.6.E43 (ebook) | DDC 616.8/047547–dc23

      LC record available at https://lccn.loc.gov/2021003276 LC ebook record available at https://lccn.loc.gov/2021003277

      Cover Design: Wiley

      Cover Image: © Andrea Danti/Shutterstock, imaginima/iStock/Getty Images, xijian/iStock/Getty Images, Marmaduke St. John/Alamy Stock Photo

      Preface to the Second Edition

      Brain research has reached a considerable level of maturity due, for example, to having access to: a wealth of recording and screening resources; availability of substantial data banks; advanced data processing algorithms; and emerging artificial intelligence (AI) for making more accurate clinical diagnosis. Neurotechnology is also now being exploited to design revolutionary interfaces to guide artificial prostheses for human rehabilitation. Moreover, the technology for brain repair, communications between live and AI‐based body parts, mind reading, and intelligent recordings together with the use of virtual and augmented reality domains is advancing remarkably. The advances in brain research will soon make the Internet‐of‐brains feasible and enable fully monitoring the body for personal medicine purposes.

      To progress this fast‐growing technology, the demand for electroencephalography (EEG) data, as a widely accessible, informative, flexible, and expandable brain screening modality, together with suitable approaches in EEG processing, is rising dramatically.

      Automatic clinical diagnosis requires signal processing and machine learning algorithms to bring more insight into interpretation of the data, devising a treatment plan, and defining the path for achieving personalized medicine which is the goal of future healthcare systems. EEG is of particular interest to researchers due to its very rich information content and its relation to the entire body function.

      EEG signals represent three fundamental activities in the brain: firstly, they show the normal brain rhythms which exist in the EEGs of healthy subjects and indicate the human states such as awake and sleep; secondly, they demonstrate the brain responses to audio, visual, and somatosensory excitations, whose variations can represent the brain performance in the cases of mental fatigue, learning, and memory load; and thirdly, the communications between various brain zones which can change due to ageing, dementia, and many other factors. The study of these three aspects of EEG is the focus of this book.

      Most of the concepts in single channel or multichannel EEG signal processing have their origin in distinct application areas such as communication, seismic, speech and music signal processing. EEG signals are generally slow‐varying waveforms and therefore, similar to many other physiological signals, can be processed online without much computational effort.

This second edition of the book EEG Signal Processing, first published in 2007, highlights the major impact machine learning is now having on EEG analysis. This has been made possible by the recent developments in data analysis: firstly, due to the availability of supercomputers, powerful graphic cards, large volume computer clusters, and memory space within the public cloud, and secondly, due to introducing powerful classification algorithms such as deep neural networks (DNNs) which are suitable for numerous applications in brain–computer interfacing, mental task evaluation, brain disorder/disease recognition, and many others.

      This edition is inclusive and comprehensive, encompassing almost all methodologies in EEG processing and learning together with their diverse applications. It is not only the result of the endeavours of our research teams, but also an encyclopaedia of the most recent works in EEG signal processing, machine learning, and their applications. Hence, this edition covers a wider, deeper, and richer content thereby alleviating the shortcomings in the first edition of this book. As such, this edition