Analyzing Neural Time Series Data: Theory and Practice
Analyzing Neural Time Series Data: Theory and Practice
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发表时间：
2014-01
影响因子：
8.6
通讯作者：
Michael X. Cohen
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文献类型：
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作者：
Michael X. Cohen
本书为分析脑电信号的理论和实践提供了全面的指南。它解释了对人类和非人类动物的脑磁图 (MEG)、脑电图 (EEG) 和局部场电位 (LFP) 记录进行基于时间、时频和同步的分析的概念、数学和实现（通过 Matlab 编程）方面。这是关于该主题的唯一一本书，涵盖了理论背景和语言实现，无需经过广泛的正式数学培训的读者（包括认知科学家、神经科学家和心理学家）就可以理解。逐章阅读本书并在 Matlab 中实现示例的读者将了解为什么进行分析以及如何进行分析、如何解释结果、方法论问题是什么以及如何进行单主题级别和组级别的分析。熟悉使用自动化程序执行高级分析的研究人员将了解单击“立即分析”按钮时会发生什么。本书提供了示例数据和可下载的 Matlab 代码。 38 章每一章涵盖一个分析主题，这些主题从简单到高级。大多数章节都以练习结尾，以进一步发展本章所涵盖的材料。所提出的许多方法（包括卷积、傅里叶变换和欧拉公式）都是基础方法，构成了其他高级数据分析方法的基础。掌握了书中方法的读者将为学习其他方法做好充分准备。
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.