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Signal Processing for Neuroscientists

Author: Wim van Drongelen
Publisher: London : Academic Press, [2018]
Edition/Format:   eBook : Document : English : Second editionView all editions and formats
Summary:
Signal Processing for Neuroscientists, Second Edition provides an introduction to signal processing and modeling for those with a modest understanding of algebra, trigonometry and calculus. With a robust modeling component, this book describes modeling from the fundamental level of differential equations all the way up to practical applications in neuronal modeling. It features nine new chapters and an exercise  Read more...
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Genre/Form: Electronic books
Additional Physical Format: (OCoLC)1013726703
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Wim van Drongelen
ISBN: 9780128104835 012810483X
OCLC Number: 1032374929
Description: 1 online resource (xx, 719 pages)
Contents: Intro; Title page; Table of Contents; Copyright; Dedication; Preface to the Second Edition; Preface to the Companion Volume; Preface to the First Edition; Chapter 1. Introduction; 1.1. Overview; 1.2. Biomedical Signals; 1.3. Biopotentials; 1.4. Examples of Biomedical Signals; 1.5. Analog-to-Digital Conversion; 1.6. Moving Signals Into the MATLAB® Analysis Environment; Appendix 1.1; Exercises; Chapter 2. Data Acquisition; 2.1. Rationale; 2.2. The Measurement Chain; 2.3. Sampling and Nyquist Frequency in the Frequency Domain; 2.4. The Move to the Digital Domain; Appendix 2.1; Exercises Chapter 3. Noise3.1. Introduction; 3.2. Noise Statistics; 3.3. Signal-to-Noise Ratio; 3.4. Noise Sources; Appendix 3.1; Appendix 3.2; Appendix 3.3; Appendix 3.4; Appendix 3.5 Laplace and Fourier Transforms of Probability Density Functions; Exercises; Chapter 4. Signal Averaging; 4.1. Introduction; 4.2. Time-Locked Signals; 4.3. Signal Averaging and Random Noise; 4.4. Noise Estimates; 4.5. Signal Averaging and Nonrandom Noise; 4.6. Noise as a Friend of the Signal Averager; 4.7. Evoked Potentials; 4.8. Overview of Commonly Applied Time Domain Analysis Techniques Appendix 4.1 Expectation of the Product of Independent Random VariablesExercises; Chapter 5. Real and Complex Fourier Series; 5.1. Introduction; 5.2. The Fourier Series; 5.3. The Complex Fourier Series; Examples; Appendix 5.1; Appendix 5.2; Exercises; Chapter 6. Continuous, Discrete, and Fast Fourier Transform; 6.1. Introduction; 6.2. The Fourier Transform; 6.3. Discrete Fourier Transform and the Fast Fourier Transform Algorithm; Exercises; Chapter 7. 1-D and 2-D Fourier Transform Applications; 7.1. Spectral Analysis; 7.2. Two-Dimensional Fourier Transform Applications in Imaging Appendix 7.1Exercises; Chapter 8. Lomb's Algorithm and Multitaper Power Spectrum Estimation; 8.1. Overview; 8.2. Unevenly Sampled Data; 8.3. Errors in the Periodogram; Appendix 8.1; Appendix 8.2; Exercises; Chapter 9. Differential Equations: Introduction; 9.1. Modeling Dynamics; 9.2. How to Formulate an Ordinary Differential Equation; 9.3. Solving First- and Second-Order Ordinary Differential Equations; 9.4. Ordinary Differential Equations With a Forcing Term; 9.5. Representation of Higher-Order Ordinary Differential Equations as a Set of First-Order Ones 9.6. Transforms to Solve Ordinary Differential EquationsExercises; Chapter 10. Differential Equations: Phase Space and Numerical Solutions; 10.1. Graphical Representation of Flow and Phase Space; 10.2. Numerical Solution of an ODE; 10.3. Partial Differential Equations; Exercises; Chapter 11. Modeling; 11.1. Introduction; 11.2. Different Types of Models; 11.3. Examples of Parametric and Nonparametric Models; 11.4. Polynomials; 11.5. Nonlinear Systems With Memory; Appendix 11.1; Exercises; Chapter 12. Laplace and z-Transform; 12.1. Introduction
Responsibility: Wim van Drongelen.

Abstract:

Signal Processing for Neuroscientists, Second Edition provides an introduction to signal processing and modeling for those with a modest understanding of algebra, trigonometry and calculus. With a robust modeling component, this book describes modeling from the fundamental level of differential equations all the way up to practical applications in neuronal modeling. It features nine new chapters and an exercise section developed by the author. Since the modeling of systems and signal analysis are closely related, integrated presentation of these topics using identical or similar mathematics presents a didactic advantage and a significant resource for neuroscientists with quantitative interest. Although each of the topics introduced could fill several volumes, this book provides a fundamental and uncluttered background for the non-specialist scientist or engineer to not only get applications started, but also evaluate more advanced literature on signal processing and modeling.

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