EEG signal classification based on simple random sampling technique with least square support vector machine
Article
Article Title | EEG signal classification based on simple random sampling technique with least square support vector machine |
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ERA Journal ID | 41226 |
Article Category | Article |
Authors | Li, Yan (Author) and Wen, Peng (Author) |
Journal Title | International Journal of Biomedical Engineering and Technology |
Journal Citation | 7 (4), pp. 390-409 |
Number of Pages | 20 |
Year | 2011 |
Publisher | Inderscience Publishers |
Place of Publication | Geneva, Switzerland |
ISSN | 1752-6418 |
1752-6426 | |
Digital Object Identifier (DOI) | https://doi.org/10.1504/IJBET.2011.044417 |
Web Address (URL) | http://www.inderscience.com/search/index.php?action=record&rec_id=44417&prevQuery=&ps=10&m=or |
Abstract | This paper proposes a new approach based on Simple Random Sampling (SRS) technique with Least Square Support Vector Machine (LS-SVM) to classify two-class of electroencephalogram (EEG) signals. The experiments are carried out on two EEG databases and a synthetic Ripley dataset. All two-class pairs are tested and our proposed approach obtains a 95.58% average classification accuracy for the EEG epileptic database, 98.73% for the mental imagery tasks EEG database and 100% for Ripley data. We compare our method with two most recent methods for the epileptic database. Experimental results demonstrate that the proposed method is more promising than previously reported classification techniques. |
Keywords | EEG; electroencephalogram; simple random sampling technique; LS-SVM; least square support vector machine; signal classification; feature extraction. |
ANZSRC Field of Research 2020 | 320602. Medical biotechnology diagnostics (incl. biosensors) |
400607. Signal processing | |
400303. Biomechanical engineering | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Department of Mathematics and Computing |
Centre for Systems Biology | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q10vq/eeg-signal-classification-based-on-simple-random-sampling-technique-with-least-square-support-vector-machine
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