Clustering technique-based least square support vector machine for EEG signal classification

Siuly, Siuly; Li, Yan; Wen, Peng

doi:10.1016/j.cmpb.2010.11.014

Cited by 206 publications

(55 citation statements)

References 29 publications

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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%

“…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%

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Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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Section: Epilepsy and Epileptic Seizure Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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“…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%

Comparisons Between Motor Area Eeg and All-Channels Eeg for Two Algorithms in Motor Imagery Task Classification

Siuly

Wen

2014

Biomed. Eng. Appl. Basis Commun.

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Abstract. This article reports on a comparative study to identify electroencephalography (EEG) signals during motor imagery (MI) for motor area EEG and all-channels EEG in the Brain Computer Interface (BCI) application. In this paper, we present two algorithms: CC-LS-SVM and CC-LR for MI tasks classification. The CC-LS-SVM algorithm combines the cross-correlation (CC) technique and the least square support vector machine (LS-SVM). The CC-LR algorithm assembles the cross-correlation (CC) technique and binary logistic regression (LR) model. These two algorithms are implemented on the motor area EEG and the all-channels EEG to investigate how well they perform and also to test which area EEG is better for the MI classification. These two algorithms are also compared with some existing methods which reveal their competitive performance during classification. Results on both datasets, IVa and IVb from BCI Competition III, show that the CC-LS-SVM algorithm performs better than the CC-LR algorithm on both the motor area EEG and the all-channels EEG. The results also demonstrate that the CC-LS-SVM algorithm performs much better for the all-channels EEG than for the motor area EEG. Furthermore, the LS-SVM based approach can correctly identify the discriminative MI tasks, demonstrating the algorithm's superiority in classification performance over some existing methods.

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“…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%

Quantization of Adulteration Ratio of Raw Cow Milk by Least Squares Support Vector Machines (LS-SVM) and Visible/Near Infrared Spectroscopy

Hsieh

Hung

Kuo

2011

Engineering Applications of Neural Networks

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Abstract. Raw cow milk has short supply market in summer and over supply in winter, which causes consumers and dairy industry concern about the quality of raw milk whether is adulated with reconstituted milk (powdered milk). This study prepared 307 raw cow milk samples with various adulteration ratios 0%, 2%, 5%, 10%, 20%, 30%, 50%, 75%, and 100% of powdered milk. Least square support vector machine (LS-SVM) was applied to calibrate the prediction model for adulteration ratio. Grid search approach was used to find the better value of network parameters of γ and σ 2 . Results show that R 2 ranges from 0.9662 to 0.9777 for testing data set with plate surface and four concave regions. Scatter plot of testing data showed that adulteration ratio above 10% clearly differs from 0% samples.

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Clustering technique-based least square support vector machine for EEG signal classification

Cited by 206 publications

References 29 publications

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Comparisons Between Motor Area Eeg and All-Channels Eeg for Two Algorithms in Motor Imagery Task Classification

Quantization of Adulteration Ratio of Raw Cow Milk by Least Squares Support Vector Machines (LS-SVM) and Visible/Near Infrared Spectroscopy

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