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EEG Signal Processing, by Saeid Sanei, Jonathon A. Chambers
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Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities. With appropriate interpretation methods they are emerging as a key methodology to satisfy the increasing global demand for more affordable and effective clinical and healthcare services.
Developing and understanding advanced signal processing techniques for the analysis of EEG signals is crucial in the area of biomedical research. This book focuses on these techniques, providing expansive coverage of algorithms and tools from the field of digital signal processing. It discusses their applications to medical data, using graphs and topographic images to show simulation results that assess the efficacy of the methods.
Additionally, expect to find:
- explanations of the significance of EEG signal analysis and processing (with examples) and a useful theoretical and mathematical background for the analysis and processing of EEG signals;
- an exploration of normal and abnormal EEGs, neurological symptoms and diagnostic information, and representations of the EEGs;
- reviews of theoretical approaches in EEG modelling, such as restoration, enhancement, segmentation, and the removal of different internal and external artefacts from the EEG and ERP (event-related potential) signals;
- coverage of major abnormalities such as seizure, and mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease, together with their mathematical interpretations from the EEG and ERP signals and sleep phenomenon;
- descriptions of nonlinear and adaptive digital signal processing techniques for abnormality detection, source localization and brain-computer interfacing using multi-channel EEG data with emphasis on non-invasive techniques, together with future topics for research in the area of EEG signal processing.
The information within EEG Signal Processing has the potential to enhance the clinically-related information within EEG signals, thereby aiding physicians and ultimately providing more cost effective, efficient diagnostic tools. It will be beneficial to psychiatrists, neurophysiologists, engineers, and students or researchers in neurosciences. Undergraduate and postgraduate biomedical engineering students and postgraduate epileptology students will also find it a helpful reference.
- Sales Rank: #1349174 in Books
- Published on: 2007-09-11
- Original language: English
- Number of items: 1
- Dimensions: 9.92" h x .88" w x 6.87" l, 1.53 pounds
- Binding: Hardcover
- 312 pages
From the Back Cover
Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities. With appropriate interpretation methods they are emerging as a key methodology to satisfy the increasing global demand for more affordable and effective clinical and healthcare services.
Developing and understanding advanced signal processing techniques for the analysis of EEG signals is crucial in the area of biomedical research. This book focuses on these techniques, providing expansive coverage of algorithms and tools from the field of digital signal processing. It discusses their applications to medical data, using graphs and topographic images to show simulation results that assess the efficacy of the methods.
Additionally, expect to find:
- explanations of the significance of EEG signal analysis and processing (with examples) and a useful theoretical and mathematical background for the analysis and processing of EEG signals;
- an exploration of normal and abnormal EEGs, neurological symptoms and diagnostic information, and representations of the EEGs;
- reviews of theoretical approaches in EEG modelling, such as restoration, enhancement, segmentation, and the removal of different internal and external artefacts from the EEG and ERP (event-related potential) signals;
- coverage of major abnormalities such as seizure, and mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease, together with their mathematical interpretations from the EEG and ERP signals and sleep phenomenon;
- descriptions of nonlinear and adaptive digital signal processing techniques for abnormality detection, source localization and brain-computer interfacing using multi-channel EEG data with emphasis on non-invasive techniques, together with future topics for research in the area of EEG signal processing.
The information within EEG Signal Processing has the potential to enhance the clinically-related information within EEG signals, thereby aiding physicians and ultimately providing more cost effective, efficient diagnostic tools. It will be beneficial to psychiatrists, neurophysiologists, engineers, and students or researchers in neurosciences. Undergraduate and postgraduate biomedical engineering students and postgraduate epileptology students will also find it a helpful reference.
About the Author
Dr. Sanei received his PhD from Imperial College of Science, Technology, and Medicine, London, in Biomedical Signal and Image Processing in 1991. His major interest is in biomedical signal and image processing, adaptive and nonlinear signal processing, pattern recognition and classification. He has had a major contribution to Electroencephalogram (EEG) analysis such as epilepsy prediction, cognition evaluation, and brain computer interface (BCI). Currently, he is involved in teaching various undergraduate and postgraduate subjects such as Real-time Signal Processing, Non-linear and Adaptive Signal & Image processing, Intelligent Signal Processing, VHDL based Digital Signal Processing, and Digital Design.
�Jonathon Chambers joined the Cardiff School of Engineering in January 2004 and leads a team of researchers involved in the analysis, design and evaluation of new algorithms for digital signal processing with application in acoustics, biomedicine and beyond 3G wireless communications, and is the Director of the Centre of Digital Signal Processing and the Group Leader of the Telecommunications and Information Technology Group.
Most helpful customer reviews
50 of 50 people found the following review helpful.
The review of the book
By Michael
I am a PhD student with major in signal processing and machine learning with applications to neuroimaging data. I must emphasize that the following review is affected by my knowledge and review criteria. So it is somewhat subjective.
This book may be the newest book on EEG signal processing. It reviewed some newly developed techniques with emphasizing on independent component analysis (ICA). Plenty of references are provided at the end of each chapter.
However, compared to many classic books in signal procesisng and EEG fields, I have to give three stars to this book, due to the following reasons:
1) Some contents that are not important are introduced with many pages, while some contents that are important are introduced briefly (note the main body of the book has only 265 pages).
For example, in the 265-page book, the authors use 34 pages to introduce the background, while use half a page (or one or two paragraphs) to introduce some important algorithms which are new to EEG signal processing and have potential applications.
Another example. We know that using ICA to detect, separate and classify P300 signals is very important in the history of EEG/ERP processing. There are numerous literature involve it. But the authors give only half a page to state it.
2) Some algorithms introduced in this book are not complete.
Through the description of the book, the reader may not well understand some algorithms, such as the Parallel Factor Analysis (Section 7.3.2), because they are introduced briefly. For some algorithms the definitions of some symbols are missed, such as the algorithm in Section 5.2.4: what's the meaning of Q? And in Section 3.1.3-3.1.4 what's the meaning of r?
For some important algorithms the authors almost directly list the equations of the algorithms without explanation, such as the one in Section 5.2.9. If there is possible for the authors to provide the second edition, I think they should add something important about the algorithms, such as the motivation of algorithms, the key steps in the development of algorithms (eg. the construction of objective function of algorithms), the physical meanings of algorithms, and their performance in practical applications and so on.
3) The selection of algorithms in each topic is strongly affected by the authors' interest and their published work.
For example, in Section 3.1.3-3.1.4 the authors use 7 pages to introduce their work in the ICASSP conference, while leaving lots of important methods (proposed by others) published in other top journals or top conferences unmentioned.
Another example: In Section 7.2.1 (Preprocessing of EEG) the authors only introduced their published work, but not introduced others' work. I think in this section some things should be added. For example, there are other preprocessing aspects in addition to artifact removal. Even in artifact removal by ICA, other methods may need to be added such as several kinds of constrained ICA, methods based on higher-order statistics (using ICA as preprocessing and then using conventional methods to remove artifact), and the automatic methods for artifact removal. Especially, the removal of eye artifact may need to be added, because removal of eye artifact is very important for the visual attention experiments.
4) Some places in the book may be not clear and may arouse readers' misunderstand, or even wrong.
For example, in page 98 the authors said, "for the EEG mixing model....the propagation velocity is equivalent to that of electromagnetic waves (300,000km/s). Therefore the delay is almost zero and the mixing model can always be considered to be instantaneous (ICA model)". It's wrong. The delay definitely exists in EEG signals, since the propagation velocity of EEG signals is very very low compared to the electromagnetic waves. And there are some literatures using convolutive ICA to process EEG signals (Neural Computation 2006, Neural Networks 2003, etc).
Another example. In Section 1.2.3 the authors put skewness and kurtosis, which are often seen as measurement of non-Gaussinity in statistics literature, in the subsection of measurement of nonstationarity. Although the authors think the skewness and kurtosis are useful in the measurement of non-stationarity of signals with time-varying distribution, they may need to give clearer explanation, and provide other conventional measurement of nonstationarity (maybe it is necessary to provide the definition of nonstationarity, since there are several definitions of it in signal processing literature).
In summary, the book is absolutely not a textbook, but a long review, such that you may not fully understand algorithms but you can look for original papers to get details according the references provided by the book.
I hope the authors can provide the second edition, in which they can balance their work and others' work, balance each contents (important contents and introductory contents), provide more details on algorithms (at least those important algorithms), include more newly developed techniques or important algorithms, and modify some errors. More importantly, they need to carefully exam their manuscript.
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