Matching Pursuit and Unification in EEG Analysis Engineering in Medicine Biology 1st Edition by Piotr Durka ISBN 9781580533195 1580533191
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Product details:
ISBN 10: 1580533191
ISBN 13: 9781580533195
Author: Piotr Durka
Table of contents:
I Some Basic Notions
Chapter 1 Signal: Going Digital
1.1 Sampling
1.2 Drawback: Aliasing
1.3 Advantage: Checksums
Chapter 2 Analysis
2.1 Inner Product-A Measure of Fit
2.2 Orthogonality
2.3 Frequency and Phase
Chapter 3 Spectrum
3.1 Example Calculations
3.2 Uncertainty Principle and Resolution
3.3 Real-World Spectra
Chapter 4 Between Time and Frequency
4.1 Spectrogram
4.2 Interpretation of the Spectrogram
4.3 Wavelets
4.4 Wigner Transform and Cross-Terms
Chapter 5
Choosing the Representation
5.1 Gabor Dictionary
5.2 Adaptive Approximation
5.3 Matching Pursuit
5.4 Time-Frequency Energy Density
References
Chapter 6
Advantages of Adaptive Approximations
6.1 Explicit Parameterization of Transients
6.2 Automatic Negotiation of Time-Frequency Tradeoff
6.3 Freedom from Arbitrary Settings
6.4 A Unified Framework
Chapter 7
Caveats and Practical Issues
7.1 Dictionary Density
7.2 Number of Waveforms in the Expansion
7.3 Statistical Bias
7.4 Limitations of Gabor Dictionaries
References
II EEG Analysis
Chapter 8
Parameterization of EEG Transients
8.1 Selecting Relevant Structures
8.2 Sleep Spindles and Slow Waves
8.3 Real-World Problems
8.4 Hypnogram and Continuous Description of Sleep
8.5 Sensitivity to Phase and Frequency
8.6 Nonoscillating Structures
8.7 Epileptic EEG Spikes
References
Chapter 9
Epileptic Seizures
9.1 Series of Spikes
9.2 Periodicity and Greedy Algorithms
9.3 Evolution of Seizures
9.4 Gabor Atom Density
References
Chapter 10 Event-Related Desynchronization and Synchronization
10.1 Conventional ERD/ERS Quantification
10.2 A Complete Time-Frequency Picture
10.3 ERD/ERS in the Time-Frequency Plane
10.4 Other Estimates of Signal’s Energy Density References
Chapter 11 Selective Estimates of Energy
11.1 ERD/ERS Enhancement
11.2 Pharmaco EEG
References
Chapter 12 Spatial Localization of Cerebral Sources
12.1 EEG Inverse Solutions
12.2 Is It a Tomography?
12.3 Selection of Structures for Localization
12.4 Localization of Sleep Spindles References
III Equations and Technical Details
Chapter 13 Adaptive Approximations and Matching Pursuit
13.1 Notation
13.2 Linear Expansions
13.3 Time-Frequency Distributions
13.4 Adaptive Time-Frequency Approximations
13.5 Matching Pursuit Algorithm
13.6 Orthogonalization
13.7 Stopping Criteria
13.8 Matching Pursuit with Gabor Dictionaries
13.9 Statistical Bias
13.10 MP-Based Estimate of Signal’s Energy Density
13.11 An Interesting Failure of the Greedy Algorithm
13.12 Multichannel Matching Pursuit
References
Chapter 14 Implementation: Details and Tricks
14.1 Optimal Phase of a Gabor Function
14.2 Product Update Formula
14.3 Sin, Cos, and Exp: Fast Calculations and Tables References
Chapter 15 Statistical Significance of Changes in the Time-Frequency Plane
15.1 Reference Epoch
15.2 Resolution Elements
15.3 Statistics
15.4 Resampling
15.5 Parametric Tests
15.6 Correction for Multiple Comparisons References
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Tags: Piotr Durka, Matching Pursuit, Unification in EEG, Analysis Engineering