Revue de presse
For years, I have wished for a comprehensive and pragmatic volume that really explained in plain English the many complex techniques that are so often used in electrophysiological research. Although there are chapters in handbooks, and even entire books on digital signal processing, none are as comprehensive, pragmatic, lucid, or entertaining as Analyzing Neural Time Series Data.
John J. B. Allen, University of Arizona
This impressive book is something I have been hoping for for years. It is meticulously organized to lead the knowledgeable novice to time series analyses from concept to actual implementation. Importantly, it is written to assume little advance knowledge of the topic, but to result in actionable understanding. Michael X Cohen has done the community a great service.
George R. Mangun, University of California, Davis
This book provides a technically rigorous, practical, and thorough survey of the major computational and statistical methods used in the time-frequency analysis of electrophysiological signals. Written in a lucid and engaging manner, Cohen s treatment will prove essential reading for both students and seasoned scholars, offering the former a clear roadmap into this exciting area of research and offering the latter an invaluable reference for nearly all of the major techniques. In putting this treatise together Cohen has done a great service for the burgeoning field of cognitive electrophysiology.
--Michael J. Kahana
, University of Pennsylvania
Présentation de l'éditeur
This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the "analyze now" button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.