EEG Signal Processing and Machine Learning. Saeid Sanei

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      1  Cover

      2  Title Page

      3  Copyright Page

      4  Preface to the Second Edition

      5  Preface to the First Edition

      6  List of Abbreviations

      7  1 Introduction to Electroencephalography 1.1 Introduction 1.2 History 1.3 Neural Activities 1.4 Action Potentials 1.5 EEG Generation 1.6 The Brain as a Network 1.7 Summary References

      8  2 EEG Waveforms 2.1 Brain Rhythms 2.2 EEG Recording and Measurement 2.3 Sleep 2.4 Mental Fatigue 2.5 Emotions 2.6 Neurodevelopmental Disorders 2.7 Abnormal EEG Patterns 2.8 Ageing 2.9 Mental Disorders 2.10 Summary References

      9  3 EEG Signal Modelling 3.1 Introduction 3.2 Physiological Modelling of EEG Generation 3.3 Generating EEG Signals Based on Modelling the Neuronal Activities 3.4 Mathematical Models Derived Directly from the EEG Signals 3.5 Electronic Models 3.6 Dynamic Modelling of Neuron Action Potential Threshold 3.7 Summary References

      10  4 Fundamentals of EEG Signal Processing 4.1 Introduction 4.2 Nonlinearity of the Medium 4.3 Nonstationarity 4.4 Signal Segmentation 4.5 Signal Transforms and Joint Time–Frequency Analysis 4.6 Empirical Mode Decomposition 4.7 Coherency, Multivariate Autoregressive Modelling, and Directed Transfer Function 4.8 Filtering and Denoising 4.9 Principal Component Analysis 4.10 Summary References

      11  5 EEG Signal Decomposition 5.1 Introduction 5.2 Singular Spectrum Analysis 5.3 Multichannel EEG Decomposition 5.4 Sparse Component Analysis 5.5 Nonlinear BSS 5.6 Constrained BSS 5.7 Application of Constrained BSS; Example 5.8 Multiway EEG Decompositions 5.9 Tensor Factorization for Underdetermined Source Separation 5.10 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain 5.11 Separation of Correlated Sources via Tensor Factorization 5.12 Common Component Analysis 5.13 Canonical Correlation Analysis 5.14 Summary References

      12  6 Chaos and Dynamical Analysis 6.1 Introduction to Chaos and Dynamical Systems 6.2 Entropy 6.3 Kolmogorov Entropy 6.4 Multiscale Fluctuation‐Based Dispersion Entropy 6.5 Lyapunov Exponents 6.6 Plotting the Attractor Dimensions from Time Series 6.7 Estimation of Lyapunov Exponents from Time Series 6.8 СКАЧАТЬ