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Table of Contents
Preface; Contents; Introduction; 1 Electroencephalogram (EEG) and Its Background; 1.1 What Is EEG?; 1.2 Generation Organism of EEG Signals in the Brain; 1.3 Characteristics and Nature of EEG Signals; 1.4 Abnormal EEG Signal Patterns; References; 2 Significance of EEG Signals in Medical and Health Research; 2.1 EEG in Epilepsy Diagnosis; 2.2 EEG in Dementia Diagnosis; 2.3 EEG in Brain Tumour Diagnosis; 2.4 EEG in Stroke Diagnosis; 2.5 EEG in Autism Diagnosis; 2.6 EEG in Sleep Disorder Diagnosis; 2.7 EEG in Alcoholism Diagnosis; 2.8 EEG in Anaesthesia Monitoring; 2.9 EEG in Coma and Brain Death.
2.10 EEG in Brain-Computer Interfaces (BCIs)2.11 Significance of EEG Signal Analysis and Classification; 2.12 Concept of EEG Signal Classification; 2.13 Computer-Aided EEG Diagnosis; References; 3 Objectives and Structures of the Book; 3.1 Objectives; 3.2 Structure of the Book; 3.3 Materials; 3.3.1 Analyzed Data; 3.3.1.1 The Epileptic EEG Data; 3.3.1.2 Dataset IVa of BCI Competition III; 3.3.1.3 Dataset IVb of BCI Competition III; 3.3.1.4 Mental Imagery EEG Data of BCI Competition III; 3.3.1.5 Ripley Data; 3.3.2 Performance Evaluation Parameters.
3.4 Commonly Used Methods for EEG Signal Classification3.4.1 Methods for Epilepsy Diagnosis; 3.4.2 Methods for Mental State Recognition in BCIs; References; Techniques for the Diagnosis of Epileptic Seizures from EEG Signals; 4 Random Sampling in the Detection of Epileptic EEG Signals; 4.1 Why Random Sampling in Epileptic EEG Signal Processing?; 4.2 Simple Random Sampling Based Least Square Support Vector Machine; 4.2.1 Random Sample and Sub-sample Selection Using SRS Technique; 4.2.2 Feature Extraction from Different Sub-samples.
4.2.3 Least Square Support Vector Machine (LS-SVM) for Classification4.3 Experimental Results and Discussions; 4.3.1 Results for Epileptic EEG Datasets; 4.3.2 Results for the Mental Imagery Tasks EEG Dataset; 4.3.3 Results for the Two-Class Synthetic Data; 4.4 Conclusions; References; 5 A Novel Clustering Technique for the Detection of Epileptic Seizures; 5.1 Motivation; 5.2 Clustering Technique Based Scheme; 5.2.1 Clustering Technique (CT) for Feature Extraction; 5.3 Implementation of the Proposed CT-LS-SVM Algorithm; 5.4 Experimental Results and Discussions.
5.4.1 Classification Results for the Epileptic EEG Data5.4.2 Classification Results for the Motor Imagery EEG Data; 5.5 Conclusions; References; 6 A Statistical Framework for Classifying Epileptic Seizure from Multi-category EEG Signals; 6.1 Significance of the OA Scheme in the EEG Signals Analysis and Classification; 6.2 Optimum Allocation-Based Framework; 6.2.1 Sample Size Determination; 6.2.2 Epoch Determination; 6.2.3 Optimum Allocation; 6.2.4 Sample Selection; 6.2.5 Classification by Multiclass Least Square Support Vector Machine (MLS-SVM); 6.2.6 Classification Outcomes.
2.10 EEG in Brain-Computer Interfaces (BCIs)2.11 Significance of EEG Signal Analysis and Classification; 2.12 Concept of EEG Signal Classification; 2.13 Computer-Aided EEG Diagnosis; References; 3 Objectives and Structures of the Book; 3.1 Objectives; 3.2 Structure of the Book; 3.3 Materials; 3.3.1 Analyzed Data; 3.3.1.1 The Epileptic EEG Data; 3.3.1.2 Dataset IVa of BCI Competition III; 3.3.1.3 Dataset IVb of BCI Competition III; 3.3.1.4 Mental Imagery EEG Data of BCI Competition III; 3.3.1.5 Ripley Data; 3.3.2 Performance Evaluation Parameters.
3.4 Commonly Used Methods for EEG Signal Classification3.4.1 Methods for Epilepsy Diagnosis; 3.4.2 Methods for Mental State Recognition in BCIs; References; Techniques for the Diagnosis of Epileptic Seizures from EEG Signals; 4 Random Sampling in the Detection of Epileptic EEG Signals; 4.1 Why Random Sampling in Epileptic EEG Signal Processing?; 4.2 Simple Random Sampling Based Least Square Support Vector Machine; 4.2.1 Random Sample and Sub-sample Selection Using SRS Technique; 4.2.2 Feature Extraction from Different Sub-samples.
4.2.3 Least Square Support Vector Machine (LS-SVM) for Classification4.3 Experimental Results and Discussions; 4.3.1 Results for Epileptic EEG Datasets; 4.3.2 Results for the Mental Imagery Tasks EEG Dataset; 4.3.3 Results for the Two-Class Synthetic Data; 4.4 Conclusions; References; 5 A Novel Clustering Technique for the Detection of Epileptic Seizures; 5.1 Motivation; 5.2 Clustering Technique Based Scheme; 5.2.1 Clustering Technique (CT) for Feature Extraction; 5.3 Implementation of the Proposed CT-LS-SVM Algorithm; 5.4 Experimental Results and Discussions.
5.4.1 Classification Results for the Epileptic EEG Data5.4.2 Classification Results for the Motor Imagery EEG Data; 5.5 Conclusions; References; 6 A Statistical Framework for Classifying Epileptic Seizure from Multi-category EEG Signals; 6.1 Significance of the OA Scheme in the EEG Signals Analysis and Classification; 6.2 Optimum Allocation-Based Framework; 6.2.1 Sample Size Determination; 6.2.2 Epoch Determination; 6.2.3 Optimum Allocation; 6.2.4 Sample Selection; 6.2.5 Classification by Multiclass Least Square Support Vector Machine (MLS-SVM); 6.2.6 Classification Outcomes.