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Wavelets in Neuroscience Second Edition 2021 [Kõva köide]

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  • Formaat: Hardback, 384 pages, kõrgus x laius: 235x155 mm, kaal: 764 g, 69 Illustrations, color; 105 Illustrations, black and white; XVI, 384 p. 174 illus., 69 illus. in color., 1 Hardback
  • Sari: Springer Series in Synergetics
  • Ilmumisaeg: 17-Jun-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030759911
  • ISBN-13: 9783030759919
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Wavelets in Neuroscience Second Edition 2021Suurem pilt
  • Formaat: Hardback, 384 pages, kõrgus x laius: 235x155 mm, kaal: 764 g, 69 Illustrations, color; 105 Illustrations, black and white; XVI, 384 p. 174 illus., 69 illus. in color., 1 Hardback
  • Sari: Springer Series in Synergetics
  • Ilmumisaeg: 17-Jun-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030759911
  • ISBN-13: 9783030759919
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030759919.html
Märksõnad:

This book illustrates how modern mathematical wavelet transform techniques offer fresh insights into the complex behavior of neural systems at different levels: from the microscopic dynamics of individual cells to the macroscopic behavior of large neural networks. It also demonstrates how and where wavelet-based mathematical tools can provide an advantage over classical approaches used in neuroscience. The authors well describe single neuron and populational neural recordings.

This 2nd edition discusses novel areas and significant advances resulting from experimental techniques and computational approaches developed since 2015, and includes three new topics:

• Detection of fEPSPs in multielectrode LFPs recordings.

•Analysis of Visual Sensory Processing in the Brain and BCI for Human Attention Control;

•Analysis and Real-time Classification of Motor-related EEG Patterns;

The book is a valuable resource for neurophysiologists and physicists familiar with nonlinear dynamical systems and data processing, as well as for graduate students specializing in these and related areas.



1 Mathematical Methods of Signal Processing in Neuroscience
1(14)
1.1 General Remarks
1(1)
1.2 Nonstationarity of Neurophysiological Data
2(2)
1.3 Wavelets in Basic Sciences and Neuroscience
4(1)
1.4 Automatic Processing of Experimental Data in Neuroscience
5(1)
1.5 Brain-Computer Interfaces
6(1)
1.6 Topics to Consider
7(3)
References
10(5)
2 Brief Tour of Wavelet Theory
15(60)
2.1 From Fourier Analysis to Wavelets
16(10)
2.2 Continuous Wavelet Transform
26(35)
2.2.1 Main Definitions. Properties of the Continuous Wavelet Transform
26(5)
2.2.2 Mother Wavelets
31(3)
2.2.3 Numerical Implementation of the Continuous Wavelet Transform
34(10)
2.2.4 Visualisation of Wavelet Spectra. Wavelet Spectra of Model Signals
44(7)
2.2.5 Phase of the Wavelet Transform
51(10)
2.3 Discrete Wavelet Transform
61(10)
2.3.1 Comparison of the Discrete and Continuous Wavelet Transforms
61(3)
2.3.2 General Properties
64(7)
References
71(4)
3 Analysis of Single Neuron Recordings
75(44)
3.1 Introduction
75(1)
3.2 Wavelet Analysis of Intracellular Dynamics
76(8)
3.2.1 Interference Microscopy and Subcellular Dynamics
76(2)
3.2.2 Modulation of High Frequency Oscillation by Low Frequency Processes
78(1)
3.2.3 Double Wavelet Transform and Analysis of Modulation
79(2)
3.2.4 Modulation of Spike Trains by Intrinsic Neuron Dynamics
81(3)
3.3 Information Encoding by Individual Neurons
84(15)
3.3.1 Vibrissae Somatosensory Pathway
84(2)
3.3.2 Classification of Neurons by Firing Patterns
86(1)
3.3.3 Drawbacks of the Traditional Approach to Information Processing
87(1)
3.3.4 Wavelet Transform of Spike Trains
88(3)
3.3.5 Dynamical Stability of the Neuronal Response
91(3)
3.3.6 Stimulus Responses of Trigeminal Neurons
94(5)
3.4 Wavelet Coherence for Spike Trains: A Way to Quantify Functional Connectivity
99(16)
3.4.1 Wavelet Coherence of Two Point Processes
100(1)
3.4.2 Measure of Functional Coupling Between Stimulus and Neuronal Response
101(2)
3.4.3 Functional Connectivity of Gracilis Neurons to Tactile Stimulus
103(12)
References
115(4)
4 Classification of Neuronal Spikes from Extracellular Recordings
119(56)
4.1 Introduction
119(1)
4.2 General Principles of Spike Sorting
120(2)
4.3 Spike Detection Over a Broadband Frequency Activity
122(3)
4.4 Naive Spike Sorting
125(3)
4.5 Principal Component Analysis as Spike-Feature Extractor
128(4)
4.5.1 How It Works
128(2)
4.5.2 Possible Pitfalls
130(2)
4.6 Wavelet Transform as Spike-Feature Extractor
132(3)
4.6.1 Wavelet Spike Classifier
133(1)
4.6.2 Potential Problems
133(2)
4.7 Wavelet Shape-Accounting Classifier
135(2)
4.8 Performance of PCA Versus WT for Feature Extraction
137(3)
4.9 Sensitivity of Spike Sorting to Noise
140(4)
4.9.1 Impact of High/Low Frequency Noise on PCA and WT
140(2)
4.9.2 Proper Noise Filtering May Improve Spike Sorting
142(2)
4.10 Optimal Sorting of Spikes with Wavelets and Adaptive Filtering
144(7)
4.10.1 Noise Statistics and Spike Sorting
145(1)
4.10.2 Parametric Wavelet Sorting with Advanced Filtering
146(5)
4.11 Spike Sorting by Artificial Neural Networks
151(9)
4.11.1 General Approach
152(2)
4.11.2 Artificial Neural Networks
154(2)
4.11.3 Training the Artificial Neural Network
156(1)
4.11.4 Algorithm for Spike Sorting Using Neural Networks
157(3)
4.12 Artificial Wavelet Neural Networks for Spike Sorting
160(11)
4.12.1 Structure of Wavelet Neural Networks
161(1)
4.12.2 Wavelet Neural Networks
161(10)
References
171(4)
5 Analysis of Gamma-Waves in Multielectrode LFP Recordings
175(36)
5.1 Introduction
175(2)
5.2 Disentanglement of Raw LFP Recordings into Pathway-Specific Generators
177(7)
5.2.1 LFP Recordings and Current-Source-Density Analysis
177(3)
5.2.2 Decomposition of LFPs into Pathway-Specific Generators
180(4)
5.3 Localization and Quantification of Gamma Waves in the Schaffer-Generator by Wavelet Analysis
184(8)
5.3.1 Method for Detecting Gamma Waves
184(4)
5.3.2 Elementary Micro-fEPSPs in Ongoing Schaffer Activity
188(2)
5.3.3 Detected Gamma Events Help to Establish Causal Relations Between CA3 and CA1 Pyramidal Cells
190(2)
5.4 Improved Identification of Micro-fEPSP Events
192(8)
5.4.1 Distortion of Micro-fEPSP Events by Wavelet Method
192(1)
5.4.2 Likelihood Enhanced Wavelet (LeW) Method
193(7)
5.5 Bilateral Integration of Gamma-Parsed Information
200(6)
5.5.1 Experimental Recordings and Retrieval of Bilateral Micro-fEPSP Events
202(1)
5.5.2 Analysis of Bilateral CA3-CA1 Pathways
203(3)
5.6 Conclusions
206(2)
References
208(3)
6 Wavelet Approach to the Study of Rhythmic Neuronal Activity
211(32)
6.1 Introduction
211(1)
6.2 Basic Principles of Electroencephalography
212(4)
6.2.1 Electrical Biopotential: From Neuron to Brain
213(1)
6.2.2 Application of EEG in Epilepsy Research
214(2)
6.3 General Principles of Time-Frequency Analysis of EEG
216(13)
6.3.1 The Need for Mathematical Analysis of EEG
216(1)
6.3.2 Time-Frequency Analysis of EEG: From Fourier Transform to Wavelets
217(4)
6.3.3 Time-Frequency Analysis of Spike-Wave Discharges by Means of Different Mother Wavelets
221(8)
6.4 Applications of Wavelets in Electroencephalography
229(6)
6.4.1 Time-Frequency Analysis of EEG Structure
230(1)
6.4.2 Automatic Detection of Oscillatory Patterns and Different Rhythms in Pre-recorded EEG
230(1)
6.4.3 Classification of Oscillatory Patterns
231(1)
6.4.4 Real-Time Detection of Oscillatory Patterns in EEG
231(1)
6.4.5 Multichannel EEG Analysis of Synchronization of Brain Activity
232(1)
6.4.6 Artifact Suppression in Multichannel EEG Using Wavelets and Independent Component Analysis
232(1)
6.4.7 Study of Cognitive Processes
233(2)
References
235(8)
7 Wavelet-Based Diagnostics of Paroxysmal Activity in EEG and Brain-Computer Interfaces for Epilepsy Control
243(60)
7.1 Introduction
243(2)
7.2 Mother Wavelet Function in the Continuous Wavelet Transform
245(2)
7.3 Detection of Spike-Wave Discharges (Absence Epilepsy) in WAG/Rij Rats
247(5)
7.4 Spindle-Like Oscillations and Spike-Wave Epilepsy
252(23)
7.4.1 Time-Frequency Analysis of Spindle-Like Oscillatory Patterns
255(7)
7.4.2 Wavelet-Based Approach for Detecting Sleep Spindles and 5-9 Hz Oscillations in EEG
262(3)
7.4.3 Classification of Normal and Abnormal Spindle Oscillations by Means of Adaptive Wavelet Analysis
265(10)
7.5 Pro-epileptic Activity and Undeveloped Spike-Wave Seizures in Genetically Prone Subjects
275(5)
7.5.1 Time-Frequency Characteristics of Pro-epileptic Patterns in EEG in WAG/Rij Rats
275(1)
7.5.2 Algorithm for the Automatic Detection of Pro-epileptic Patterns in EEG
276(4)
7.6 Brain-Computer Interface for On-Line Diagnostics of Epileptic Seizures
280(7)
7.6.1 On-Line SWD Detection Algorithm
281(3)
7.6.2 Experimental Verification of the Algorithm and On-Line SWD Diagnostics
284(3)
7.7 Brain Stimulation Brain-Computer Interface for Prediction and Prevention of Epileptic Seizures
287(7)
7.7.1 Precursor Wavelet-Based On-Line Detection
287(4)
7.7.2 Absence Seizure Control by a Brain Computer Interface
291(3)
References
294(9)
8 Analysis of Visual Sensory Processing in the Brain and Brain-Computer Interfaces for Human Attention Control
303(48)
8.1 Introduction
303(3)
8.2 Ambigous Stimuli as a Tool to Study Visual Perception
306(2)
8.3 Local and Integrative Neural Activity During Visual Sensory Processing
308(16)
8.3.1 Local Activity
309(6)
8.3.2 Functional Connectivity
315(9)
8.4 Visual Sensory Processing and the Human Factors
324(7)
8.4.1 Different Scenarios of Visual Perception
325(2)
8.4.2 Spectral Properties of the Different Scenarios
327(2)
8.4.3 Single-Trial Analysis
329(2)
8.5 BCIs for the Control of Human Condition During Sensory Processing Tasks
331(13)
8.5.1 Wavelet-Based Approach to Estimate Attention in BCI
332(2)
8.5.2 Testing the Feedback Effect
334(3)
8.5.3 Cognitive Load Distribution via BCI
337(7)
References
344(7)
9 Analysis and Real-Time Classification of Motor-Related EEG and MEG Patterns
351(32)
9.1 Real and Imagery Movements
351(9)
9.1.1 Wavelet-Transform Modulus Maxima (WTMM)
353(4)
9.1.2 Time--Frequency Analysis
357(3)
9.2 Visual and Kinestetic Motor Imagery
360(6)
9.2.1 Wavelet Analysis
362(1)
9.2.2 Cluster Analysis
362(3)
9.2.3 Neurophysiological Aspects of Motor Imagery
365(1)
9.3 Age-Related Distinctions in EEG Signals During Execution of Motor Tasks Characterized in Terms of Wavelet Spectra
366(11)
9.3.1 Experimental Study and Motor Brain Response Time Analysis
367(2)
9.3.2 Time-Frequency Analysis of Brain Response on Motor Activity
369(5)
9.3.3 Classification of Wavelet Spectra by Machine Learning Techniques
374(3)
References
377(6)
10 Conclusion
383
Reference
384