Signal Processing for Neuroscientists /

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 equa...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Drongelen, Wim van (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, [2018]
Edición:Second edition.
Temas:
Signal processing > Digital techniques.
Neurosciences > Data processing.
Neurology > Mathematical models.
Neurosciences.
Neurosciences
Traitement du signal > Techniques num�eriques.
Neurologie > Mod�eles math�ematiques.
HEALTH & FITNESS > Diseases > General.
MEDICAL > Clinical Medicine.
MEDICAL > Diseases.
MEDICAL > Evidence-Based Medicine.
MEDICAL > Internal Medicine.
Electronic books.
Internet Resources.
Charts.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

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