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Analyzing Neural Time Series Data: Theory and Practice (The
MIT Press)
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business account Series: The MIT Press Hardcover: 600 pages Publisher: The MIT Press; 1 edition (January
17, 2014) Language: English ISBN-10: 9780262019873 ISBN-13: 978-0262019873 ASIN: 0262019876
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Book Description
A comprehensive guide to the conceptual, mathematical, and implementational aspects of
analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. 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 grouplevel
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.
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