EEG Signal Analysis and Classification

Chapter 1 Electroencephalogram (EEG) and Its Background

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 11 Comparative Study: Motor Area EEG and All-Channels EEG

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