Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Computer aided diagnosis of ECG data on the least square support vector machine
Digital Signal Processing
Computers in Biology and Medicine
Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines
Computers in Biology and Medicine
Wavelet/mixture of experts network structure for EEG signals classification
Expert Systems with Applications: An International Journal
Journal of Medical Systems
Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals
Computers in Biology and Medicine
Automatic seizure detection based on time-frequency analysis and artificial neural networks
Computational Intelligence and Neuroscience - Regular issue
Combined neural network model employing wavelet coefficients for EEG signals classification
Digital Signal Processing
Statistics over features: EEG signals analysis
Computers in Biology and Medicine
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Epileptic seizure detection using dynamic wavelet network
Expert Systems with Applications: An International Journal
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Application of SVM framework for classification of single trial EEG
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
Computer Methods and Programs in Biomedicine
Computers in Biology and Medicine
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The electroencephalogram (EEG) signal captures the electrical activity of the brain and is an important source of information for studying neurological disorders. The proper analysis of this biological signal plays an important role in the domain of brain-computer interface, which aims at the construction of communication channels between human brain and computers. In this paper, we investigate the application of least squares support vector machines (LS-SVM) to the task of epilepsy diagnosis through automatic EEG signal classification. More specifically, we present a sensitivity analysis study by means of which the performance levels exhibited by standard and least squares SVM classifiers are contrasted, taking into account the setting of the kernel function and of its parameter value. Results of experiments conducted over different types of features extracted from a benchmark EEG signal dataset evidence that the sensitivity profiles of the kernel machines are qualitatively similar, both showing notable performance in terms of accuracy and generalization. In addition, the performance accomplished by optimally configured LS-SVM models is also quantitatively contrasted with that obtained by related approaches for the same dataset.