2011
DOI: 10.1016/j.cmpb.2010.11.014
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Clustering technique-based least square support vector machine for EEG signal classification

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Cited by 209 publications
(57 citation statements)
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“…For the detection of epilepsy and seizure, Adeli et al [32] developed a wavelet chaos methodology for analysis of EEGs and delta, theta, alpha, beta and gamma sub-bands of EEGs. Siuly et al [5] introduced a clustering technique-based LS-SVM for EEG signal classification. Akin et al [33] tried to find a new solution for diagnosing the epilepsy.…”
Section: Epilepsy and Epileptic Seizure Diagnosismentioning
confidence: 99%
See 1 more Smart Citation
“…For the detection of epilepsy and seizure, Adeli et al [32] developed a wavelet chaos methodology for analysis of EEGs and delta, theta, alpha, beta and gamma sub-bands of EEGs. Siuly et al [5] introduced a clustering technique-based LS-SVM for EEG signal classification. Akin et al [33] tried to find a new solution for diagnosing the epilepsy.…”
Section: Epilepsy and Epileptic Seizure Diagnosismentioning
confidence: 99%
“…Supporting medical experts or neurologists in the process of finding a correct diagnosis to a hypothesis in a timely manner is very desirable to improve a patient's outcome. In general, the analysis of those vast amounts of information is performed manually through visual inspection by neurologists/experts to identify and understand abnormalities from medical imaging and signal data [5]. The visual inspection of such huge data is not a satisfactory procedure for precise and reliable diagnosis as it is time-consuming, error prone and subject to fatigue.…”
Section: Introductionmentioning
confidence: 99%
“…As accuracy is a major concern in BCI systems, this study uses the classification accuracy as the criterion to evaluate the performance of the proposed method. The classification accuracy is calculated by dividing the number of correctly classified samples by the total number of samples [27,32,34]. It is worthy to mention that all experimental results for datasets, IVa and IVb, are presented based on the testing set.…”
Section: Resultsmentioning
confidence: 99%
“…Thirdly, we divide the feature vector set randomly as the training set and the testing set using the 3-fold cross-validation method [32,33] in both the motor cortex set and the all-channels data, separately. In the 3-fold cross-validation procedure, a feature vector set is partitioned into 3 mutually exclusive subsets of approximately equal size and the method is repeated 3 times (folds).…”
Section: Data and Implementationmentioning
confidence: 99%
“…Yu et al [16] compared partial least squares regression (PLSR) with LS-SVM in alcohol content, titratable acidity, and pH prediction and found LS-SVM was slightly better. Siuly and Wen [17] used LS-SVM to cluster EEG signal. Zuao et al [18] employed on-line LS-SVM for gas prediction.…”
Section: Introductionmentioning
confidence: 99%