Klienditugi: 7440010 (E-R 10-18)

E-raamat: Wavelets in Neuroscience

Alexander E. Hramov, Alexey N. Pavlov, Vladimir A. Maksimenko, Evgenia Sitnikova, Valeri A. Makarov, Alexey A. Koronovskii

Formaat: PDF+DRM
Sari: Springer Series in Synergetics
Ilmumisaeg: 16-Jun-2021
Kirjastus: Springer Nature Switzerland AG
Keel: eng
ISBN-13: 9783030759926

Teised raamatud teemal:

Formaat - PDF+DRM
Hind: 184,63 €*
* hind on lõplik, st. muud allahindlused enam ei rakendu
Lisa ostukorvi
Lisa soovinimekirja
See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.

Formaat: PDF+DRM
Sari: Springer Series in Synergetics
Ilmumisaeg: 16-Jun-2021
Kirjastus: Springer Nature Switzerland AG
Keel: eng
ISBN-13: 9783030759926

Teised raamatud teemal:

DRM piirangud

Kopeerimine (copy/paste):

ei ole lubatud
Printimine:

ei ole lubatud
Kasutamine:

Digitaalõiguste kaitse (DRM)
Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

Vajalik tarkvara
Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

Seda e-raamatut ei saa lugeda Amazon Kindle's.

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

(14)

1.1 General Remarks

(1)

1.2 Nonstationarity of Neurophysiological Data

(2)

1.3 Wavelets in Basic Sciences and Neuroscience

(1)

1.4 Automatic Processing of Experimental Data in Neuroscience

(1)

1.5 Brain-Computer Interfaces

(1)

1.6 Topics to Consider

(3)

References

(5)

2 Brief Tour of Wavelet Theory

(60)

2.1 From Fourier Analysis to Wavelets

(10)

2.2 Continuous Wavelet Transform

(35)

2.2.1 Main Definitions. Properties of the Continuous Wavelet Transform

(5)

2.2.2 Mother Wavelets

(3)

2.2.3 Numerical Implementation of the Continuous Wavelet Transform

(10)

2.2.4 Visualisation of Wavelet Spectra. Wavelet Spectra of Model Signals

(7)

2.2.5 Phase of the Wavelet Transform

(10)

2.3 Discrete Wavelet Transform

(10)

2.3.1 Comparison of the Discrete and Continuous Wavelet Transforms

(3)

2.3.2 General Properties

(7)

References

(4)

3 Analysis of Single Neuron Recordings

(44)

3.1 Introduction

(1)

3.2 Wavelet Analysis of Intracellular Dynamics

(8)

3.2.1 Interference Microscopy and Subcellular Dynamics

(2)

3.2.2 Modulation of High Frequency Oscillation by Low Frequency Processes

(1)

3.2.3 Double Wavelet Transform and Analysis of Modulation

(2)

3.2.4 Modulation of Spike Trains by Intrinsic Neuron Dynamics

(3)

3.3 Information Encoding by Individual Neurons

(15)

3.3.1 Vibrissae Somatosensory Pathway

(2)

3.3.2 Classification of Neurons by Firing Patterns

(1)

3.3.3 Drawbacks of the Traditional Approach to Information Processing

(1)

3.3.4 Wavelet Transform of Spike Trains

(3)

3.3.5 Dynamical Stability of the Neuronal Response

(3)

3.3.6 Stimulus Responses of Trigeminal Neurons

(5)

3.4 Wavelet Coherence for Spike Trains: A Way to Quantify Functional Connectivity

(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

Lisainfo e-raamatute kohta

Püsilink: https://www.kriso.ee/db/97830307599262e.html

Märksõnad:

E-raamat: Wavelets in Neuroscience

DRM piirangud

Kopeerimine (copy/paste):

Printimine:

Kasutamine:

Konto & seaded

Otsing

Otsingu andmebaas

Filtreeri tulemusi

Teemad E-raamatute teemad

Vali ostukorv