Analyzing Neural Time Series Data: Theory and Practice

Analyzing Neural Time Series Data offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. The text 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 implementation in a language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists.

Readers can review the book chapter by chapter and implement the examples in MATLAB to 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. 

The book provides sample data and downloadable MATLAB code. Each of the 38 chapters covers one analysis topic, with topics ranging 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.