13 7 Machine Learning for EEG Analysis 7.1 Introduction 7.2 Clustering Approaches 7.3 Classification Algorithms 7.4 Common Spatial Patterns 7.5 Summary References
14 8 Brain Connectivity and Its Applications 8.1 Introduction 8.2 Connectivity through Coherency 8.3 Phase‐Slope Index 8.4 Multivariate Directionality Estimation 8.5 Modelling the Connectivity by Structural Equation Modelling 8.6 Stockwell Time–Frequency Transform for Connectivity Estimation 8.7 Inter‐Subject EEG Connectivity 8.8 State‐Space Model for Estimation of Cortical Interactions 8.9 Application of Cooperative Adaptive Filters 8.10 Graph Representation of Brain Connectivity 8.11 Tensor Factorization Approach 8.12 Summary References
15 9 Event‐Related Brain Responses 9.1 Introduction 9.2 ERP Generation and Types 9.3 Detection, Separation, and Classification of P300 Signals 9.4 Brain Activity Assessment Using ERP 9.5 Application of P300 to BCI 9.6 Summary References
16 10 Localization of Brain Sources 10.1 Introduction 10.2 General Approaches to Source Localization 10.3 Head Model 10.4 Most Popular Brain Source Localization Approaches 10.5 Forward Solutions to the Localization Problem 10.6 The Methods Based on Source Tracking 10.7 Determination of the Number of Sources from the EEG/MEG Signals 10.8 Other Hybrid Methods 10.9 Application of Machine Learning for EEG/MEG Source Localization 10.10 Summary References
17 11 Epileptic Seizure Prediction, Detection, and Localization 11.1 Introduction 11.2 Seizure Detection 11.3 Chaotic Behaviour of Seizure EEG 11.4 Seizure Detection from Brain Connectivity 11.5 Prediction of Seizure Onset from EEG 11.6 Intracranial and Joint Scalp–Intracranial Recordings for IED Detection 11.7 Fusion of EEG–fMRI Data for Seizure Prediction 11.8 Summary References
18 12 Sleep Recognition, Scoring, and Abnormalities 12.1 Introduction 12.2 Stages of Sleep 12.3 The Influence of Circadian Rhythms 12.4 Sleep Deprivation 12.5 Psychological Effects 12.6 EEG Sleep Analysis and Scoring 12.7 Detection and Monitoring of Brain Abnormalities during Sleep by EEG and Multimodal PSG Analysis 12.8 Dreams and Nightmares 12.9 EEG and Consciousness 12.10 Functional Brain Connectivity for Sleep Analysis 12.11 Summary References
19 13 EEG‐Based Mental Fatigue Monitoring 13.1 Introduction 13.2 Feature‐Based Machine Learning Approaches 13.3 Measurement of Brain Synchronization and Coherency 13.4 Evaluation of ERP for Mental Fatigue 13.5 Separation of P3a and P3b 13.6 A Hybrid EEG–ERP‐Based Method for Fatigue Analysis Using an Auditory Paradigm 13.7 Assessing Mental Fatigue by Measuring Functional Connectivity 13.8 Deep Learning Approaches for Fatigue Evaluation 13.9 Summary References
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