Classification of EEG signals using sampling techniques and least square support vector machines
Paper
Paper/Presentation Title | Classification of EEG signals using sampling techniques and least square support vector machines |
---|---|
Presentation Type | Paper |
Authors | Li, Yan (Author) and Wen, Peng (Author) |
Editors | Wen, Peng |
Journal or Proceedings Title | Lecture Notes in Computer Science (Book series) |
Journal Citation | 5589, pp. 375-382 |
Number of Pages | 8 |
Year | 2009 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1611-3349 |
0302-9743 | |
ISBN | 9783642029615 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-642-02962-2_47 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-642-02962-2_47 |
Conference/Event | 4th International Conference on Rough Sets and Knowledge Technology (RSKT 2009) |
Event Details | 4th International Conference on Rough Sets and Knowledge Technology (RSKT 2009) Parent International Conference on Rough Sets and Knowledge Technology (RSKT) Event Date 14 to end of 16 Jul 2009 Event Location Gold Coast, Australia |
Abstract | This paper presents sampling techniques (ST) concept for feature extraction from electroencephalogram (EEG) signals. It describes the application of least square support vector machine (LS-SVM) that executes the classification of EEG signals from two classes, namely normal persons with eye |
Keywords | sampling techniques (ST); simple random sampling (SRS); least square support vector machines (LS-SVM); electroencephalogram (EEG) |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
400607. Signal processing | |
490102. Biological mathematics | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Department of Mathematics and Computing |
Centre for Systems Biology |
https://research.usq.edu.au/item/9z741/classification-of-eeg-signals-using-sampling-techniques-and-least-square-support-vector-machines
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