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Analyzing neural time series data.

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Books boeken mike cohen press hardcover januari 2014: 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 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 Eulers 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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Analyzing neural time series data.

Analyzing

Neural

Series

Boeken

Books

Time series analysis.

Canonical auto and cross correlations of multivariate time series.

Introduction to time series analysis and forecasting.

Bayesian time series models.

Population and community biology series space time and geometry.