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EEG Signal Analysis and Classification Techniques and Applications
Author
Title
EEG Signal Analysis and Classification [electronic resource] : Techniques and Applications / by Siuly Siuly, Yan Li, Yanchun Zhang.
ISBN
9783319476537
Edition
1st ed. 2016.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2016.
Physical Description
1 online resource (XIII, 256 p.) 96 illus.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ENIN.
Other formats
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Printed edition:
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Format
Books / Online
Language
English
Added to Catalog
October 06, 2022
Series
Health Information Science,
Contents
Electroencephalogram (EEG) and its background
Significance of EEG signals in medical and health research
Objectives and structures of the book
Random sampling in the detection of epileptic EEG signals
A novel clustering technique for the detection of epileptic seizures
A statistical framework for classifying epileptic seizure from multi-category EEG signals
Injecting principal component analysis with the OA scheme in the epileptic EEG signal classification
Cross-correlation aided logistic regression model for the identification of motor imagery EEG signals in BCI applications
Modified CC-LR Algorithm for identification of MI based EEG signals
Improving prospective performance in the MI recognition: LS-SVM with tuning hyper parameters
Comparative study: Motor area EEG and All-channels EEG
Optimum allocation aided Naive Bayes based learning process for the detection of MI tasks
Summary discussions on the methods, future directions and conclusions.
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