Chapter 1 Electroencephalogram (EEG) and Its Background

Chapter 2 Significance of EEG Signals in Medical and Health Research

Chapter 3 Objectives and Structures of the Book

Chapter 4 Random Sampling in the Detection of Epileptic EEG Signals

Chapter 5 A Novel Clustering Technique for the Detection of Epileptic Seizures

Chapter 6 A Statistical Framework for Classifying Epileptic Seizure from Multi-category EEG Signals

Chapter 7 Injecting Principal Component Analysis with the OA Scheme in the Epileptic EEG Signal Classification

Chapter 8 Cross-Correlation Aided Logistic Regression Model for the Identification of Motor Imagery EEG Signals in BCI Applications

Chapter 9 Modified CC-LR Algorithm for Identification of MI-Based EEG Signals

Chapter 10 Improving Prospective Performance in MI Recognition: LS-SVM with Tuning Hyper Parameters

Chapter 12 Optimum Allocation Aided Naïve Bayes Based Learning Process for the Detection of MI Tasks

Chapter 13 Summary Discussion on the Methods, Future Directions and Conclusions