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Adaptive Processing of Brain Signals

 E-Book
Sofort lieferbar | Lieferzeit:3-5 Tage I
ISBN-13:
9781118622155
Einband:
E-Book
Seiten:
472
Autor:
Saeid Sanei
eBook Typ:
PDF
eBook Format:
E-Book
Kopierschutz:
Adobe DRM [Hard-DRM]
Sprache:
Englisch
Beschreibung:

In this book, the field of adaptive learning and processing is extended to arguably one of its most important contexts which is the understanding and analysis of brain signals. No attempt is made to comment on physiological aspects of brain activity; instead, signal processing methods are developed and used to assist clinical findings. Recent developments in detection, estimation and separation of diagnostic cues from different modality neuroimaging systems are discussed.
These include constrained nonlinear signal processing techniques which incorporate sparsity, nonstationarity, multimodal data, and multiway techniques.

Key features:
* Covers advanced and adaptive signal processing techniques for the processing of electroencephalography (EEG) and magneto-encephalography (MEG) signals, and their correlation to the corresponding functional magnetic resonance imaging (fMRI)
* Provides advanced tools for the detection, monitoring, separation, localising and understanding of functional, anatomical, and physiological abnormalities of the brain
* Puts a major emphasis on brain dynamics and how this can be evaluated for the assessment of brain activity in various states such as for brain-computer interfacing emotions and mental fatigue analysis
* Focuses on multimodal and multiway adaptive processing of brain signals, the new direction of brain signal research
Inhaltsangabe

CHAPTER 1 BRAIN SIGNALS, THEIR GENERATION, ACQUISITION AND PROPERTIES

1.1 Introduction

1.2 Historical Review of the Brain

1.3 Neural Activities

1.4 Action Potentials

1.5 EEG Generation

1.6 Brain Rhythms

1.7 EEG Recording and Measurement

1.7.1 Conventional EEG Electrode Positioning

1.7.2 Conditioning the Signals

1.8 Abnormal EEG Patterns

1.9 Aging

1.10 Mental Disorders

1.10.1 Dementia

1.10.2 Epileptic Seizure and Nonepileptic Attacks

1.10.3 Psychiatric Disorders

1.10.4 External Effects

1.11 Memory and Content Retrieval

1.12 MEG Signals and their Generation

1.13 Summary and Conclusions

References

CHAPTER 2 FUNDAMENTALS OF EEG SIGNAL PROCESSING

2.1 Introduction

2.2 Nonlinearity of the Medium

2.3 Nonstationarity

2.4 Signal Segmentation

2.5 Other properties of brain signals

2.6 Summary and Conclusions

References

CHAPTER 3 EEG SIGNAL MODELLING

3.1 Physiological Modelling of EEG generation:

3.1.1 Integrate and Fire Models

3.1.2 Phase-Coupled Models

3.1.3 Hodgkin and Huxley Model

3.2 Mathematical Models

3.2.1 Linear Models

3.2.1.1 Prediction method

3.2.1.2 Prony’s method

3.2.2 Nonlinear Modelling

3.2.3 Gaussian Mixture Model

3.3 Generating EEG Signals Based on Modelling the Neuronal Activities

3.4 Electronic Models

3.4.1 Models Describing the Function of the Membrane

3.4.1.1 Lewis Membrane Model

3.4.1.2 Roy Membrane Model

3.4.2 Models Describing the Function of Neuron

3.4.2.1 Lewis Neuron Model

3.4.2.2 Harmon Neuron Model

3.4.3 A Model Describing the Propagation of Action Pulse in Axon

3.4.4 Integrated Circuit Realizations

3.5 Dynamic Modelling of Neuron Action Potential Threshold

3.6 Summary and Conclusion

References

CHAPTER 4 SIGNAL TRANSFORMS AND JOINT TIME-FREQUENCY ANALYSIS

4.1 Introduction

4.2 Parametric Spectrum Estimation and Z-Transform

4.3 Time – Frequency Domain Transforms

4.3.1 Short Time Fourier Transform

4.3.2 Wavelet Transform

4.3.2.1 Continuous wavelet transform

4.3.2.2 Examples of continuous wavelets

4.3.2.3 Discrete time wavelet transform

4.3.3 Multiresolution analysis

4.3.3.1 Wavelet transform using Fourier transform

4.3.3.2 Reconstruction

4.4 Ambiguity Function and the Wigner-Ville Distribution

4.5 Hermite Transform

4.6 Concluding Remarks

References

CHAPER 5 CHAOS AND DYNAMICAL ANALYSIS

5.1 Entropy

5.2 Kolmogorov Entropy

5.3 Lyapunov Exponents

2.4 Plotting the Attractor Dimensions from Time Series

5.5 Estimation of Lyapunov Exponents from Time Series

5.5.1 Optimum time delay

5.5.2 Optimum embedding dimension

5.6 Approximate Entropy

5.7 Using Prediction Order

5.8 Conclusions

References

CHAPTER 6 CLASSIFICATION AND CLUSTERING O Factorization for BSS

7.10.2 Solving BSS of Nonstationary Sources using Tensor Factorization

7.11 Tensor Factorization for Underdetermined Source Separation

7.12 Tensor Factorization for Separation of Convolutive Mixtures in Time Domain

7.13 Separation of Correlated Sources via Tensor Factorization

7.14 Summary and Conclusions

References

CHAPTER 8 CONNECTIVITY OF BRAIN REGIONS

8.1 Introduction

8.2 Connectivity through Coherency

8.3 Phase-Slope Index

8.4 Multivariate Directionality Estimation

8.4.1 Directed Transfer Function

8.5Modelling the Connectivity by Structural Equation Modelling

8.6 EEG Hyper-scanning and Inter-subject Connectivity

8.6.1 Objectives

8.6.2 Technological Relevance

8.7 State-Space Model for Estimation of Cortical Interactions

8.8.1 Use of Kalman Filter

8.8.2 Task-related Adaptive Connectivity

8.8.3 Diffusion Adaptation

8.9 Tensor Factorization Approach

8.10 Summary and Conclusions

References

CHAPTER 9 DETECTION AND TRACKING OF EVENT RELATED POTENTIALS

9.1 ERP Generation and Types

9.1.1 P300 and its Subcomponents

9.2 Detection, Separation, and Classification of P300 Signals

9.2.1 Using ICA

9.2.2 Estimation of Single Trial Brain Responses by Modelling the ERP Waveforms

9.2.3 ERP Source Tracking in Time

9.2.4 Time-Frequency Domain Analysis

9.2.5 Application of Kalman Filter

9.2.6 Particle Filtering and its Application to ERP Tracking

9.2.7 Variational Bayes Method

9.2.8 Prony’s Approach for Detection of P300 Signals

9.2.9 Adaptive Time-Frequency Methods

9.3 Brain Activity Assessment Using ERP

9.4 Application of P300 to BCI

9.5 Summary and Conclusions

References

CHAPTER 10 MENTAL FATIGUE

10.1 Introduction

10.2 Measurement of Brain Synchronization and Coherency

10.2.1 Linear Measure of Synchronization

10.2.2 Nonlinear Measure of Synchronization

10.3 Evaluation of ERP for Mental Fatigue

10.4 Separation of P3a and P3b

10.5 A Hybrid EEG-ERP-Based Method for Fatigue Analysis Using an Auditory Paradigm

10.6 Conclusions

REFERENCES

CHAPTER 11 EMOTION ENCODING, REGULATION AND CONTROL

11.1 Theories and Emotion Classification

11.2 The Effects of Emotions

11.3 Psychology and Psychophysiology of Emotion

11.4 Emotion Regulation

11.5 Emotion-Provoking Stimuli

11.6 Change in the ERP and Normal Brain Rhythms

11.6.1 ERP and Emotion:

11.6.2 Changes of Normal Brain Waves with Emotion:

11.7 Perception of Odours and Emotion: why are they related?

11.8 Emotion-related Brain Signal Processing

11.9 Other Neuroimaging Modalities used for Emotion Study

11.10 Applications

11.11 Concluding Remarks

References

CHAPTER 12 SLEEP AND SLEEP APNEA

12.1  Introduction

12.2 Stages of Sleep

12.2.1 NREM Sleep

12.2.2 REM Sleep

12.3 The Influence of Circadian Rhythms

12.4 Sleep Deprivation

12.5 Psychological Effects

12.6 D Movement

13.3.4 Gamma Band Oscillations

13.3.5 Long Delta Activity

13.4 Major Problems in BCI

13.4.1 Pre-processing of the EEGs

13.5 Multi-Dimensional EEG Decomposition

13.5.1 Space-Time-Frequency Method

13.5.2 Parallel Factor Analysis

13.6 Detection and Separation of ERP Signals

13.7 Estimation of Cortical Connectivity

13.8 Application of Common Spatial Patterns

13.9 Multiclass Brain Computer Interfacing

13.10 Cell-Cultured BCI

13.11 Summary and Conclusions

References

CHAPTER 14 EEG AND MEG SOURCE LOCALIZATION

14.1 Introduction

14.2 General Approaches to Source Localization

14.2.1 Dipole Assumption

14.3 Most Popular Brain Source Localization Approaches

14.3.1 ICA Method

14.3.2 MUSIC Algorithm

14.3.3 LORETA Algorithm

14.3.4 FOCUSS Algorithm

14.3.5 Standardised LORETA

14.3.6 Other Weighted Minimum Norm Solutions

14.3.7 Evaluation Indices

14.3.8 Joint ICA-LORETA Approach

14.3.9 Partially Constrained BSS Method

14.3.10 Constrained Least Squares Method for localization of P3a and P3b

14.3.11 Spatial Notch Filtering Approach

14.3.12 Deflation Beamforming Approach for EEG/MEG Multiple Source Localization

14.3.13 Hybrid Beamforming – Particle Filtering

14.4 Determination of the Number of Sources from the EEG/MEG Signals

14.5 Summary and Conclusions

References

CHAPTER 15 SEIZURE AND EPILEPSY

15.1 Introduction

15.2 Types of Epilepsy

15.3 Seizure Detection

15.3.1 Adult Seizure Detection

15.3.2 Detection of Neonate Seizure

15.4 Chaotic Behaviour of EEG Sources

15.5 Predictability of Seizure from EEG

15.6 Fusion of EEG – fMRI data for Seizure Detection and Prediction

15.7 Summary and Conclusions

References

CHAPTER 16 JOINT ANALYSIS OF EEG AND FMRI

16.1 Fundamental Concepts

16.1.1 Blood oxygenation level dependent

16.1.2 Popular fMRI Data Formats

16.1.3 Pre-processing of fMRI Data

16.

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