Saeid Sanei - EEG Signal Processing and Machine Learning

1. Chapter 5
2. Chapter 9
3. Chapter 10
4. Chapter 12
5. Chapter 18

1. Chapter 1
2. Chapter 2
3. Chapter 3
4. Chapter 4
5. Chapter 5
6. Chapter 6
7. Chapter 7
8. Chapter 8
9. Chapter 9
10. Chapter 10
11. Chapter 11
12. Chapter 12
13. Chapter 13
14. Chapter 14
15. Chapter 15
16. Chapter 16
17. Chapter 17
18. Chapter 18

Second Edition

Saeid Sanei

Imperial College London & Nottingham Trent University, UK

Jonathon A. Chambers

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 CataloginginPublication 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 616.8/047547dc23

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

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 AIbased 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 Internetofbrains feasible and enable fully monitoring the body for personal medicine purposes.

To progress this fastgrowing 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 slowvarying 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 braincomputer interfacing, mental task evaluation, brain disorder/disease recognition, and many others.