Analysis and classification of EEG signals using a hybrid clustering technique
Paper
Paper/Presentation Title | Analysis and classification of EEG signals using a hybrid clustering technique |
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Presentation Type | Paper |
Authors | Li, Y. (Author) and Wen, P. (Author) |
Editors | Li, Yan, Yang, Jiajia, Wen, Peng and Wu, Jinglong |
Journal or Proceedings Title | Proceedings of the 2010 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2010) |
ERA Conference ID | 50435 |
Number of Pages | 6 |
Year | 2010 |
Place of Publication | Piscataway, NJ. United States |
ISBN | 9781424468416 |
9781424468430 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICCME.2010.5558875 |
Web Address (URL) of Paper | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5558875 |
Conference/Event | 2010 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2010) |
ICME International Conference on Complex Medical Engineering | |
Event Details | 2010 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2010) Parent ICME International Conference on Complex Medical Engineering Event Date 13 to end of 15 Jul 2010 Event Location Gold Coast, Australia |
Event Details | ICME International Conference on Complex Medical Engineering CME |
Abstract | This paper presents a novel hybrid approach based on clustering technique (CT) and least square support vector machine (LS-SVM) denoted as CT-LS-SVM for classifying two-class EEG signals. The study aims to extract representative features from the original EEG data through the CT method and then to classify two-class EEG signals by the LS-SVM using these features as inputs. In order to test the effectiveness of the proposed method, the experiment is carried out on an epileptic EEG data and a mental imagery tasks EEG data. The classification accuracy of the current method is compared to the previous reported methods of the literature. The proposed approach is found to achieve an average classification accuracy of 99.19% for the mental imagery tasks EEG data and 94.18% for the epileptic EEG data. Our results show the highest classification accuracy (99.90%) for healthy subjects with eyes open (Set A) and epileptic patients during seizure activity (Set E) from the epileptic EEG data among the reported algorithms. Thus, the findings of the current research demonstrate that the CT method is efficient for extracting features representing the EEG signals and the LS-SVM classifier has the inherent ability to solve a pattern recognition task for these features. |
Keywords | CT method; EEG signals; clustering technique; hybrid clustering technique; image classification; least square support vector machine; pattern recognition |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
320602. Medical biotechnology diagnostics (incl. biosensors) | |
400607. Signal processing | |
Public Notes | © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | Department of Mathematics and Computing |
Department of Electrical, Electronic and Computer Engineering |
https://research.usq.edu.au/item/q031w/analysis-and-classification-of-eeg-signals-using-a-hybrid-clustering-technique
2022
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