Overview
- Presents innovative methodologies in two emerging fields, including epileptic seizure detection and mental state identification for brain computer interface
- Discusses the applications of developed methods in real-time benchmark databases and provides experimental evaluation results to assess the efficacy of such methods
- Shows researchers and practitioners how to improve the existing systems to increase benefits for medical analysis and management
Part of the book series: Health Information Science (HIS)
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About this book
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 developedmethodologies 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.
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 developedmethodologies 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.
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Keywords
- Electroencephalogram (EEG)
- Epileptic seizure
- Feature extraction
- Classification
- Brain computer interface (BCI)
- Motor imagery (MI)
- Clustering technique (CT)
- Simple random sampling (SRS)
- Cross-correlation (CC) technique
- Optimum allocation technique
- Least square supper vector machine (LS-SVM)
- Logistic regression (LR)
- Kernal logistic regression (KLR)
- Optimum allocation sampling
- k-NN
- Multinomial logistic regression with a ridge estimator
- Support vector machine (SVM)
- Naive Bayes method
Table of contents (13 chapters)
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Front Matter
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Techniques for the Diagnosis of Epileptic Seizures from EEG Signals
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Front Matter
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Methods for Identifying Mental States in Brain Computer Interface Systems
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Front Matter
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Discussions, Future Directions and Conclusions
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Front Matter
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Authors and Affiliations
Bibliographic Information
Book Title: EEG Signal Analysis and Classification
Book Subtitle: Techniques and Applications
Authors: Siuly Siuly, Yan Li, Yanchun Zhang
Series Title: Health Information Science
DOI: https://doi.org/10.1007/978-3-319-47653-7
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2016
Hardcover ISBN: 978-3-319-47652-0Published: 10 January 2017
Softcover ISBN: 978-3-319-83791-8Published: 25 July 2018
eBook ISBN: 978-3-319-47653-7Published: 03 January 2017
Series ISSN: 2366-0988
Series E-ISSN: 2366-0996
Edition Number: 1
Number of Pages: XIII, 256
Number of Illustrations: 96 b/w illustrations
Topics: Signal, Image and Speech Processing, Health Informatics, Artificial Intelligence, Biomedical Engineering and Bioengineering, Image Processing and Computer Vision, Information Systems Applications (incl. Internet)