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E-grāmata: Brief Survey of Quantitative EEG

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Brief Survey of Quantitative EEGPalielināt
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This book covers various quantitative methods for preprocessing and analyzing human EEG signals. It presents a holistic approach to quantitative EEG from its neurological basis to simultaneous EEG and fMRI studies. Equal emphasis is given to major mathematical and statistical theories and computational techniques that have been in use in qEEG and their applications on clinical and laboratory experimental EEG. The book is compact and self-contained, requiring no background in EEG processing or acquisition and quantitative techniques.

Preface xi
Acknowledgments xv
1 Neurophysiology of the Human Scalp EEG
1(24)
1.1 Neural Basis of EEG
2(6)
1.1.1 Dipole Source Model
4(2)
1.1.2 Distributed Source Model
6(2)
1.2 Tissue Impedance
8(2)
1.3 Artifacts
10(3)
1.3.1 Physiologic Artifacts
10(2)
1.3.2 Extraphysiologic Artifacts
12(1)
1.4 Electrode Placement Systems
13(3)
1.4.1 10-20 System
13(2)
1.4.2 10-10 System
15(1)
1.5 Noise
16(1)
1.6 Data Representation
17(1)
1.7 Frequency Bands
18(7)
1.7.1 Delta
19(1)
1.7.2 Theta
19(1)
1.7.2.1 Alpha
20(1)
1.7.2.2 Mu
21(1)
1.7.3 Beta
21(1)
1.7.4 Gamma
22(1)
References
23(2)
2 Preprocessing
25(26)
2.1 Filtering
25(13)
2.1.1 Impulse Response Filter
27(5)
2.1.2 Butterworth Low-Pass Filter
32(3)
2.1.3 Gaussian Low-Pass Filter
35(1)
2.1.4 Band-Pass Filter
36(2)
2.2 Decomposition Techniques
38(13)
2.2.1 Principal Component Analysis
38(2)
2.2.2 Independent Component Analysis
40(7)
2.2.3 Gist of PCA and ICA Comparison
47(1)
References
48(3)
3 Source Localization
51(26)
3.1 Forward Problem
51(18)
3.1.1 Boundary Element Method
51(1)
3.1.1.1 Dipole Source Model
52(5)
3.1.1.2 Distributed Source Model
57(2)
3.1.2 Finite Element Method
59(4)
3.1.3 Finite Difference Method
63(1)
3.1.3.1 iFDM
64(1)
3.1.3.2 aFDM
65(3)
3.1.4 Comparison among Methods
68(1)
3.2 Inverse Problem
69(8)
3.2.1 Weighted Minimum Norm Inverse
69(2)
3.2.2 MUSIC
71(2)
3.2.3 R-MUSIC
73(1)
3.2.4 sLORETA
74(1)
References
75(2)
4 Event-Related Potential
77(16)
4.1 Plotting ERP Data
77(4)
4.2 Measuring ERP Amplitudes
81(2)
4.2.1 Peak Amplitude
81(2)
4.2.2 Mean Amplitude
83(1)
4.3 Measuring ERP Latencies
83(2)
4.3.1 Peak Latency
83(1)
4.3.2 Fractional Area Latency
84(1)
4.4 Analyses of ERP
85(8)
4.4.1 ANOVA in ERP
85(3)
4.4.2 MANOVA in ERP
88(3)
References
91(2)
5 Binding Problem
93(24)
5.1 Synchronization
94(23)
5.1.1 Phase Synchronization
96(1)
5.1.1.1 Hilbert Transformation Based
97(6)
5.1.1.2 Wavelet Transformation Based
103(2)
5.1.1.3 Fourier Transformation Based
105(2)
5.1.2 Other Synchronizations
107(2)
5.1.3 Multivariate Analysis
109(6)
References
115(2)
6 Epilepsy Research
117(36)
6.1 Automatic Seizure Detection
117(19)
6.1.1 Template-Based Seizure Detection
119(1)
6.1.1.1 Feature Extraction
119(2)
6.1.1.2 Representation of Seizure Onset Patterns
121(2)
6.1.1.3 Distance Measure
123(1)
6.1.1.4 Onset Detection
124(1)
6.1.2 Transformation-Based Detection
125(4)
6.1.3 Operator-Based Detection
129(2)
6.1.3.1 False-Detection Avoidance
131(5)
6.2 Lateralization
136(2)
6.3 Interictal EEG
138(5)
6.4 Seizure Prediction
143(4)
6.5 ROC Curve Analysis
147(6)
References
150(3)
7 Brain-Computer Interface
153(44)
7.1 Preprocessing and Signal Enhancement
155(1)
7.2 Frequency Domain Features
156(6)
7.3 Time Domain Features
162(1)
7.4 Signal Analysis
163(3)
7.5 Translation Algorithms
166(31)
7.5.1 Fisher's Linear Discriminant
166(10)
7.5.2 Logistic Regression
176(2)
7.5.3 Support Vector Machine
178(10)
7.5.4 Neural Network
188(5)
7.5.5 k-Means Clustering
193(2)
References
195(2)
8 An Overview of fMRI
197(24)
8.1 Magnetic Resonance Imaging
197(13)
8.1.1 T1-Weighted Imaging
198(4)
8.1.2 T2-Weighted Imaging
202(3)
8.1.3 Spatial Localization
205(5)
8.2 Imaging Functional Activity
210(1)
8.3 The BOLD Effect
211(1)
8.4 Interpreting the BOLD Response
212(9)
References
219(2)
9 Simultaneous EEG and fMRI
221(26)
9.1 Artifacts
222(10)
9.1.1 fMRI Gradient Artifact
223(7)
9.1.2 Cardioballistogram and Blood Flow Effect
230(2)
9.2 Recording Principles
232(2)
9.2.1 EEG Wire
233(1)
9.2.2 Movement
234(1)
9.3 Interpretation
234(13)
9.3.1 Converging Evidence
235(3)
9.3.2 Direct Data Fusion
238(2)
9.3.3 Computational Neural Modeling
240(3)
References
243(4)
Appendix A Fourier Transformation 247(6)
Appendix B Wavelet Transformation 253(4)
Index 257
Kaushik MaJumdar is an Assistant Professor at the Indian Statistical Institute, Bangalore, India.
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Permanent link: https://www.kriso.lv/db/97814398961742e.html
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