EEG signal analysis and classification: techniques and applications
Authored book
Book Title | EEG signal analysis and classification: techniques and applications |
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Book Category | Authored book |
ERA Publisher ID | 3337 |
Authors | Siuly, Siuly (Author), Li, Yan (Author) and Zhang, Yanchun (Author) |
Number of Pages | 257 |
Series | Health Information Science |
Year | 2017 |
Publisher | Springer |
Place of Publication | Switzerland |
ISBN | 9783319476520 |
9783319476537 | |
ISSN | 2366-0988 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-47653-7 |
Web Address (URL) | https://link.springer.com/book/10.1007%2F978-3-319-47653-7 |
Abstract | 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. |
Keywords | EEG analysis, signal classification, machine learning methods, optimum allocation, support vector machines, random sampling |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Victoria University |
School of Agricultural, Computational and Environmental Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q3x0q/eeg-signal-analysis-and-classification-techniques-and-applications
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