EEG Signal Analysis and Classification : Techniques and Applications / by Siuly Siuly, Yan Li, Yanchun Zhang.

Format:

Book

Author/Creator:

Siuly, Siuly, author.

Li, Yan (Of University of Southern Queensland), author.

Zhang, Yanchun, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Health information science 2366-0988

Health Information Science, 2366-0988

Language:

English

Subjects (All):

Signal processing.

Image processing.

Speech processing systems.

Medical informatics.

Artificial intelligence.

Biomedical engineering.

Optical data processing.

Application software.

Signal, Image and Speech Processing.

Health Informatics.

Artificial Intelligence.

Biomedical Engineering and Bioengineering.

Image Processing and Computer Vision.

Information Systems Applications (incl. Internet).

Local Subjects:

Signal, Image and Speech Processing.

Health Informatics.

Artificial Intelligence.

Biomedical Engineering and Bioengineering.

Image Processing and Computer Vision.

Information Systems Applications (incl. Internet).

Physical Description:

1 online resource (XIII, 256 pages) : 96 illustrations.

Edition:

First edition 2016.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2016.

System Details:

text file PDF

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.

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.

Other Format:

Printed edition:

ISBN:

978-3-319-47653-7

9783319476537

Access Restriction:

Restricted for use by site license.