Signal Processing for Neuroscientists
Autor Wim Van Drongelenen Limba Engleză Paperback – 29 dec 2006
* Multiple color illustrations are integrated in the text
* Includes an introduction to biomedical signals, noise characteristics, and recording techniques
* Basics and background for more advanced topics can be found in extensive notes and appendices
* A Companion Website hosts the MATLAB scripts and several data files:
http: //www.elsevierdirect.com/companion.jsp?ISBN=9780123708670"
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Specificații
Cuprins
1. Introduction
2. Data Acquisition
3. Noise
4. Signal Averaging
5. Real and Complex Fourier Series
6. Continuous, Discrete, and Fast Fourier Transform
7. 1D and 2D Fourier Transform Applications
8. Lomb’s Algorithm and Multi-Taper Power Spectrum Estimation
9. Differential Equations: Introduction
10. Differential Equations: Phase Space and Numerical Solutions
11. Modeling
12. Laplace and z-Transform
13. LTI Systems, Convolution, Correlation, Coherence, and the Hilbert Transform
14. Causality
15. Introduction to Filters: The RC-Circuit
16. Filters: Analysis
17. Filters: Specification, Bode Plot, and Nyquist Plot
18. Filters: Digital Filters
19. Kalman Filter
20. Spike Train Analyses
21. Wavelet Analysis: Time Domain Properties
22. Wavelet Analysis: Frequency Domain Properties
23. Low Dimensional Nonlinear Dynamics: Fixed Points, Limit Cycles and Bifurcations
24. Volterra Series
25. Wiener Series
26. Poisson-Wiener Series
27. Nonlinear Techniques
28. Decomposition of Multi-Channel Data
29. Modeling Neural Systems: Cellular Models
30. Modeling Neural Systems: Network Models
2. Data Acquisition
3. Noise
4. Signal Averaging
5. Real and Complex Fourier Series
6. Continuous, Discrete, and Fast Fourier Transform
7. 1D and 2D Fourier Transform Applications
8. Lomb’s Algorithm and Multi-Taper Power Spectrum Estimation
9. Differential Equations: Introduction
10. Differential Equations: Phase Space and Numerical Solutions
11. Modeling
12. Laplace and z-Transform
13. LTI Systems, Convolution, Correlation, Coherence, and the Hilbert Transform
14. Causality
15. Introduction to Filters: The RC-Circuit
16. Filters: Analysis
17. Filters: Specification, Bode Plot, and Nyquist Plot
18. Filters: Digital Filters
19. Kalman Filter
20. Spike Train Analyses
21. Wavelet Analysis: Time Domain Properties
22. Wavelet Analysis: Frequency Domain Properties
23. Low Dimensional Nonlinear Dynamics: Fixed Points, Limit Cycles and Bifurcations
24. Volterra Series
25. Wiener Series
26. Poisson-Wiener Series
27. Nonlinear Techniques
28. Decomposition of Multi-Channel Data
29. Modeling Neural Systems: Cellular Models
30. Modeling Neural Systems: Network Models