EEG Signal Analysis and Classification: Techniques and Applications

✍ Scribed by Siuly Siuly, Yan Li, Yanchun Zhang (auth.)

• Publisher: Springer International Publishing
• Year: 2016
• Tongue: English
• Leaves: 257
• Series: Health Information Science
• Edition: 1
• Category: Library

✦ Synopsis

This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems. The proposed methods enable the extraction of this vital information from EEG signals in order to accurately detect abnormalities revealed by the EEG. New methods will relieve the time-consuming and error-prone practices that are currently in use.
Common signal processing methodologies include wavelet transformation and Fourier transformation, but these methods are not capable of managing the size of EEG data. Addressing the issue, this book examines new EEG signal analysis approaches with a combination of statistical techniques (e.g. random sampling, optimum allocation) and machine learning methods. The developed methods provide better results than the existing methods. The book also offers applications of the developed methodologies that have been tested on several real-time benchmark databases.
This book concludes with thoughts on the future of the field and anticipated research challenges. It gives new direction to the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from its suggested improvements to the current computer-aided based diagnostic systems for the precise analysis and management of EEG signals.

✦ Table of Contents

Front Matter....Pages i-xiii
Front Matter....Pages 1-1
Electroencephalogram (EEG) and Its Background....Pages 3-21
Significance of EEG Signals in Medical and Health Research....Pages 23-41
Objectives and Structures of the Book....Pages 43-61
Front Matter....Pages 63-63
Random Sampling in the Detection of Epileptic EEG Signals....Pages 65-82
A Novel Clustering Technique for the Detection of Epileptic Seizures....Pages 83-97
A Statistical Framework for Classifying Epileptic Seizure from Multi-category EEG Signals....Pages 99-125
Injecting Principal Component Analysis with the OA Scheme in the Epileptic EEG Signal Classification....Pages 127-150
Front Matter....Pages 151-151
Cross-Correlation Aided Logistic Regression Model for the Identification of Motor Imagery EEG Signals in BCI Applications....Pages 153-172
Modified CC-LR Algorithm for Identification of MI-Based EEG Signals....Pages 173-188
Improving Prospective Performance in MI Recognition: LS-SVM with Tuning Hyper Parameters....Pages 189-209
Comparative Study: Motor Area EEG and All-Channels EEG....Pages 211-225
Optimum Allocation Aided Naïve Bayes Based Learning Process for the Detection of MI Tasks....Pages 227-243
Front Matter....Pages 245-245
Summary Discussion on the Methods, Future Directions and Conclusions....Pages 247-256

✦ Subjects

Signal, Image and Speech Processing;Health Informatics;Artificial Intelligence (incl. Robotics);Biomedical Engineering;Image Processing and Computer Vision;Information Systems Applications (incl. Internet)