Advanced Signal Processing on Brain Event Related Potentials Fengyu Cong, Tapani Ristaniemi, Heikki Lyytinen, Word Scientific Publishing Co. Pte. Ltd. ISBN: 978 981 4623 08 7 (978 981 4623 10 0 as ebook)
This book introduces different approaches for the analysis of event related potential (ERP) by processing the electroencephalographic signal (EEG) from the traditional and basic methods to the more complex and innovative ones. The analysis and interpretation of ERP is extremely important in cognitive neuroscience but at the same time it is complex and challenging, due to the need for separating efficiently the ERP from the surrounding EEG activity, and difficulties in distinguishing among its various components. In this book, the authors explore and explain the advanced signal processing techniques that are mainly applied for ERP characterization. The book includes both the theoretical description of these techniques and many examples from experimental practice. These examples are useful in order to easily understand the advantages and disadvantages of each technique. The reading of the first two chapters is mandatory for everyone who wants to be introduced to the ERP signal processing analysis. After defining the motivation for writing the book, together with a brief relation of the conventional ERP processing methods, the authors review the basic theory of digital signal processing: from the basic filtering techniques to the Fourier and Wavelet transforms. The authors simplify the explanation of all those concepts by associating them to the notion of correlation. This description method gives a background for readers who do not have in depth knowledge of mathematics, signal filtering and processing. In Chapter 3, the independent component analysis (ICA) is described as a tool for extracting ERPs' components from averaged EEG data based mainly on time, frequency and space domain filtering. The chapter is mostly focused on the variance correction of the ICA and on the estimation of the optimal number of components to be extracted. Several practical examples and useful recommendations of how to apply ICA are also provided. In chapter 4, the authors describe the ERP feature extraction by using tensor decomposition with non negative constraints of the time frequency representations. The authors discuss how this tool permits to extract and analyze simultaneously the main features of the ERP such as amplitude and duration. Furthermore, the properties of time frequency representation associated to the analysis of multi channel data are used in order to build ERP tensors that include time, frequency, space, stimuli and subject modes. How this decomposition permits to deeply characterize all these properties of ERP simultaneously and how to find common ERP features in one of the domains is clearly presented and discussed. Finally, chapter 5 reports the results of the recent studies performed by applying the tensor decomposition for the analysis of auditory evoked potentials produced by music. This chapter is especially attractive, with beautiful illustrations demonstrating the capability of the previously introduced tensor techniques for determining the reliability of brain activities during naturalistic music listening. In a nice figure, the authors depict the results of the selected components that resulted to be correlated with musical features, showing their behavior in spatial and frequency domains. In general, the reading is easy and comfortable with no grammatical or typographical mistakes. The book contains different levels of style and many examples and illustrations. In this way, it is suitable for neuroscientists and researchers that are both experts and without in depth knowledge of time series analysis and statistics. The chapters are very well organized, containing theoretical aspects followed by the presentation of the methods and many examples and discussions. They usually end with a summary paragraph that is very useful for the synthesis of the messages that can be distilled from the whole chapter. An important aspect is that the authors provide the Matlab code for their methods, which would permit reproducibility of their experiments. The long list of references permits to go more in depth to the concept introduced in each chapter. Figures are clear, with an excellent definition and labeling. The book reflects on the differences among the various aspects of the ERP analysis in which we can be involved. By considering all of the different techniques, the complexity of the EEG signal and all of the inter individual differences, quantifying all the components of brain activity that follow a sensory input seems an objective almost impossible to achieve. However, the works reported in this book open new solutions and contribute to define new ways to confront with these big challenges. In summary, this book makes an enjoyable reading for those that deal with some of the aspects of evoked potential for their clinical or research work and are interested in learning about other aspects of such a widespread subject. It might involve clinicians, researchers in neurology, in signal processing methods, in statistics and other specialists and researchers in the field.
Clinical Neurophysiology